The Centre for Australian Weather and Climate Research
A partnership between CSIRO and the Bureau of Meteorology
Proposal to Ministry of Earth Sciences,
Government of India
"Monsoon Mission"
Revision 1 – 18 December 2012
Evaluation and Improvement of the Unified Model
for Short- and Medium-Range Prediction
of Monsoon Rain Systems
ii
1. Title of the proposed project :
Evaluation and Improvement of the Unified Model for Short- and Medium-Range
Prediction of Monsoon Rain Systems
2. Brief information about Principal Investigator (PI) and Co-PI(s) :
PI:
Name:Date of birth:Institution:Official website:E-mail Id:Qualification:-
Dr. Elizabeth Ebert
24 February 1960
CAWCR
http://cawcr.gov.au/, http://cawcr.gov.au/staff/eee/
e.ebert@bom.gov.au
PhD (Meteorology)
Co - PI (1):
Name:Date of birth:Institution:Official website:E-mail Id:Qualification:-
Dr. Noel Davidson
10 December 1948
CAWCR
http://cawcr.gov.au/
n.davidson@bom.gov.au
PhD (Mathematical Sciences)
Co - PI (2):
Name:Date of birth:Institution:Official website:E-mail Id:Qualification:-
Dr. Kamal Puri
3 August 1945
CAWCR
http://cawcr.gov.au/
k.puri@bom.gov.au
PhD (Physics)
Project Director:
Name:Institution:Official website:E-mail Id:Qualification:-
Dr. E.N. Rajagopal
NCMRWF
http://www.ncmrwf.gov.in
rajagopal@ncmrwf.gov.in
PhD
3. Project Summary (1 page) :
(a) Intellectual merits of the proposed work
Fundamental components of the tropical circulation over India and Australia during
summer are the monsoon, tropical cyclones and monsoon depressions. They produce
much of the rainfall over these regions, and occasionally are associated with heavy rain
and high impact weather. However rainfall, particularly heavy rainfall, is difficult to
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forecast and predictions are sensitive to components of numerical forecast systems. It is
thus necessary to study prediction of these events in detail and also quantify the
uncertainties in the forecasts.
The aims of this project are to improve prediction of heavy rain events and to provide
information on the reliability of the predictions. Even though research has been
conducted on these phenomena (e.g., Davidson et al. 1998, Bohra et al. 2006) our
understanding and prediction of these weather phenomena are still somewhat limited. It is
our belief that the numerical experimentation planned for this project will improve both
our prediction and understanding of these weather phenomena.
The proposal described here provides an end-to-end research plan for:
1. Model verification and diagnostic evaluation of the current state-of-the-art of
rainfall prediction for the monsoon and embedded weather systems.
2. Numerical experimentation and sensitivity studies of selected rain events (e.g.,
monsoon onset, monsoon depressions, cyclones).
3. Application of ensemble methods to quantify the uncertainties in prediction of
heavy rain.
The numerical system to be used is the UK Met Office's Unified Model (UM) as
implemented at NCMRWF, India and CAWCR, Australia. The use of a common
modelling framework means that improvements in tropical rainfall prediction achieved
through this work will benefit both partners, and indeed, the larger community of UM
users. The application of advanced verification methods will better diagnose the sources
and nature of the errors, thus providing information that is useful to modellers for
targeting model improvements, and to forecasters when interpreting model output as part
of the operational forecast process. This proposal also explores the nature of, and
potential enhancements to, the predictability of monsoon weather through ensemble
prediction.
(b) Broader impacts of the proposed work
Improved prediction of monsoon, tropical cyclones and monsoon depressions will be of
social, humanitarian, financial and scientific benefit to India and Australia by reducing
the loss of life and property resulting from poor prediction of high impact weather. This
will result from model improvements leading to greater accuracy, and also from
quantitative uncertainty estimates enabling more informed decision making.
The proposed collaboration will strengthen scientific ties between India and Australia,
specifically enabling both NCMRWF and CAWCR to jointly build their capability in
numerical weather prediction and monsoon rainfall processes, and more broadly, helping
to pave the way to a deep and productive relationship in coming years.
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Project Description:
1. Research Objectives
The summer monsoon, including monsoon lows, depressions, and cyclones, is the main
source of rainfall in the tropical regions of India and Australia. However, our ability to
accurately forecast these circulations is hampered by gaps in our understanding of these
flows, incomplete observations of the atmospheric and surface state, and limitations in
the abilities of numerical models to simulate the relevant processes.
The objective of this proposal is to improve the numerical prediction of tropical heavy
rain associated with the monsoon circulations in short to medium range (1- to 5-day)
forecasts from the Unified Model (UM), which has been adopted by both the NCMRWF
and CAWCR for numerical weather prediction (NWP).
The proposed strategy is to first understand the systematic nature of the model's rainfall
errors in order to target areas for improvement, then conduct numerical experimentation
on a selection of tropical severe weather cases to investigate and improve the model's
physical processes. The potential of ensemble forecasting to enhance prediction of
tropical heavy rain in the medium range will also be explored. Both the Indian and
Australian UM implementations over both countries' tropical domains will be employed
to increase the model and observation data available to the project, and ensure that the
collaboration provides the greatest benefit for the partner agencies.
Specifically, we propose to address the following questions:
(1) What is the magnitude and nature of rainfall prediction errors from the UM for
the monsoon onset, and for organised weather systems such as monsoon depressions
and tropical cyclones in Australia and India?
The first step in improving the model is to understand the nature of the errors. This
component of the project will implement a suite of diagnostic verification techniques
developed in CAWCR (Ebert and McBride 2000; Ebert 2008) and use them to establish a
benchmark against which model improvements can be measured.
CAWCR and NCMRWF will jointly examine the performance of the global and regional
UM over India and tropical Australia for two monsoon seasons in 2011 and 2012, which
is sufficient to get a good picture of systematic errors in the model. The model's rainfall
performance will be quantitatively assessed as a function of rain intensity, model lead
time, and synoptic type and scale (synoptic, sub-synoptic, mesoscale, orographic), using
daily rain gauge analyses as ground truth.
In addition to the traditional metrics normally used in NWP evaluation, diagnostic spatial
verification approaches will be applied to analyse the sources of model error (e.g.,
location errors due to errors in model dynamics, intensity and structural errors related to
model physics). Forecasters will be provided with accuracy information on the UM to
help guide their use of the model output, and their subjective evaluations of the model
rainfall will also be assessed. The evaluation of global and regional models from both
centres will also offer insight into the impact of different model resolutions and
initalisation approaches on the rainfall performance.
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(2) What is the sensitivity of rainfall prediction in the UM to (a) system resolution,
(b) physical parameterizations of moist processes, surface properties (sea surface
temperature, soil moisture), boundary layer exchange and radiation?
In order to optimise the performance of the UM, numerical experiments must be
conducted to understand its sensitivity to the grid resolution and to the parameterizations
used to represent the physical processes involved in convection and rainfall generation.
This requires much greater focus on the details of the simulations, and is better done in
case study mode. We will select some interesting cases of monsoon heavy rainfall, which
will include monsoon depressions and tropical cyclones, and conduct a detailed
examination of the structure of the clouds and rainfall. We will use traditional and spatial
diagnostic approaches to evaluate surface rainfall, and TRMM, CloudSat and CALIPSO
data for validating the model cloud and rain vertical profiles.
The process of closely examining the model output could lead to improved understanding
of monsoon rainfall structure and evolution, and to the development of some diagnostics
that could be used for characterising and evaluating the simulations.
(3) What additional value, in terms of understanding predictability and providing
advance warning and decision support for tropical heavy rain, is possible through
the use of ensemble prediction?
For lead times beyond the short range, the predictability of the atmosphere is much lower
and single model runs are accompanied by a large amount of uncertainty. Ensemble
prediction is a widely used strategy for exploring the predictability of the weather under
different conditions, and for generating a suite of uncertainty products to accompany the
forecast in order to provide information to assist decision makers in mitigating risk.
NCMRWF plans to implement the MOGREPS (Met Office Global and Regional
Ensemble Prediction System), based on the UM, during the time of this proposal.
CAWCR has already implemented this ensemble system (called AGREPS) in
experimental mode, and is investigating its ability to provide advance warnings of high
impact weather in Australia. We will produce scenario and probabilistic forecasts of
monsoon rainfall from MOGREPS in hindcast mode, and work with NCMRWF to
evaluate its ability to produce reliable and skilful forecasts of tropical heavy rain in the
medium range.
1.1 Intellectual merit of the proposed work
This proposal addresses a fundamental need of the Indian government to have access to
the most timely and accurate predictions of monsoon weather and rainfall for use in water
resource management, public safety, agriculture, industry, etc. A decision has been made
to implement the Met Office Unified Model, which is among the top two or three
atmospheric models available in the world. However, it is not possible simply to import
the model and start running it and expect it to produce accurate forecasts. A significant
amount of testing of configurations and settings, as well as development of improved
model parameterizations, is required to achieve optimal performance from the model. In
the Australian context, CAWCR scientists put in a considerable and continuing effort to
improve the ACCESS model (our implementation of the UM), which became operational
in 2009 and is now undergoing a major upgrade.
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Beyond the technical aspects of model implementation, which are not insignificant, a
large amount of scientific work is involved. There must be continual assessment of the
quality of the model's predictions during the development cycle to identify real
improvements. This proposal calls for the implementation of state-of-the-art
verification methods that go beyond simply quantifying the errors, to better
diagnose the sources and nature of the errors, thus providing information that is
useful to modellers for targeting model improvements, and to forecasters when
interpreting model output as part of the operational forecast process.
Achieving the greatest accuracy from the model calls for experimentation in several
areas. Most straightforward are tests of different model resolutions, which enable
scientists to evaluate the benefits of finer resolution on the representation of physical
processes and their interactions with local topography, as well as the increased computing
resources required to achieve those finer scale predictions. Tests of the model's physical
parameterizations are more complex, and require scientists to have a strong knowledge of
the physical processes themselves. The UM has not been optimised for tropical
prediction, so special attention to tropical convection and monsoon processes is needed.
As seasonal and intraseasonal predictability is largely driven by low latitude processes,
efforts in this area are doubly important. This proposal addresses the need for better
simulation of low latitude processes by conducting numerical experimentation to
improve the representation of physical processes related to monsoon rainfall.
Forecast uncertainty increases with time into the forecast. This is particularly true at
lower latitudes where the scales of meteorological forcing are often much smaller and
weaker than in mid-latitudes, making them more difficult for models to predict.
Quantifying this uncertainty is important for making best use of the forecasts. Ensemble
prediction is the paradigm used by the major global meteorological centres to lengthen
the useful duration of a forecast by providing quantitative uncertainty information in the
form of scenarios and probabilistic forecasts. This proposal explores how the
predictability of monsoon weather could potentially be enhanced through ensemble
prediction by assessing the quality of UM-based ensemble rainfall forecasts.
1.2 Broader Impact of proposed work
The direct impacts of this research will be to
 provide internal and external users of UM numerical forecasts with quantitative
information on the accuracy and uncertainty associated with model forecasts of
monsoon rainfall in the short- to medium-range (1-5 days), thus enabling more
informed use of UM-based products, and establishing a baseline against which
model improvements can be assessed.
 focus NCMRWF's and CAWCR's scientific efforts on determining optimum
model configurations and improving its physical processes, leading to improved
model accuracy of monsoon-related heavy rain,
 lengthen the useful lead time of forecasts through ensemble prediction.
Broader impacts of this research include
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 providing NCMRWF expertise in tropical processes, as well as data and model
improvements, to the consortium of users of the Unified Model, thus improving
tropical prediction capability for all users,
 strengthening ties between NCMRWF and CAWCR, thus enhancing bilateral
scientific exchange,
 capacity building through challenging and collaborative research.
 social and economic benefit due to reduced loss of life and property through
improved prediction of monsoon, tropical cyclones and monsoon depressions.
2. Research methods
2.1 The Unified Model at NCMRWF and CAWCR
Both CAWCR and NCMRWF use the Met Office Unified Model (UM) and data
assimilation system which incorporates three-dimensional and four-dimensional
variational data assimilation (3DVAR and 4DVAR). The two systems are referred to as
the Met Office Unified Modelling Systems (UMS). Detailed justification of the UMS for
ACCESS and key references are given in Puri (2012).
The UM forms the atmospheric module of the fully coupled earth system model, the
Australian Community Climate and Earth System Simulator (ACCESS), that has been
jointly developed by the Bureau of Meteorology and CSIRO within CAWCR. ACCESS
takes a national approach to climate and weather prediction model development that is
grounded on well-engineered and realistically achievable software and supported by high
quality IT infrastructure. For CAWCR the Met Office systems, together with components
developed at the Bureau and CSIRO, offer considerable advantages for applications to
both weather prediction and climate/climate change research. Some of the key features of
the UMS include (i) use of governing equations that are non-hydrostatic, (ii) use of a
semi-implicit/semi-Lagrangian scheme to solve the governing equations, (iii) design of
the dynamical core that includes conservation of mass, mass-weighted potential
temperature and moisture, and angular momentum, and (iv) a number of alternative
formulations for the physical parametrisations. Atmospheric data assimilation using a
variational scheme (incorporating 3DVAR and 4DVAR) is designed to be used by both
global and limited area models. A further key feature of the UMS is that it can be used
for both global and limited area domains and hence can be used across space scales
ranging from mesoscale to large (climate) scales. Finally the Met Office NWP and
climate models are recognized as leading models in the field.
At NCMRWF, Observation Processing System (OPS, Version 27.1.0), four dimensional
variational data assimilation system (4D-VAR, Version 27.1.0) and Unified Model (UM,
Version 7.7) are the main components of the Unified Model global forecast suite. The
OPS prepares quality controlled observations for 4D-VAR in the desired format. 4DVAR system produces the analysis, which is the best estimate of the atmospheric state,
used as the initial condition for the UM forecast. The OPS system processes and packs
six hourly data, centered at 00, 06, 12 and 18 UTC for the four data assimilation cycles
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(00, 06, 12 & 18 UTC cycles). The 4D-VAR data assimilation is carried out for all cycles
to produce the analysis valid for these cycles. The UM short forecast run are also carried
out based on this analysis for all the four cycles, which in turn are used as the background
(first guess) for the next cycle in OPS and 4D-VAR. A deterministic 168 hr forecast is
prepared everyday based on 00 UTC analysis.
2.2 Diagnostic verification methods for rainfall
When verifying forecasts of rain systems, any error in the location of the rainfall will
usually lead to very poor apparent performance using traditional metrics, even if the
forecast was otherwise skilful according to a forecaster's or modellers' subjective
judgement. As the grid spacing of numerical models has become increasingly finer, the
impact of small location errors becomes more pronounced (Mass et al. 2002).
In recent years new verification approaches have been developed to assess gridded
forecasts in a spatial sense, to better replicate human judgements about their skill.
CAWCR has been active in the development of two types of spatial verification. The
Contiguous Rain Area (CRA) method of Ebert and McBride (2000) verifies properties of
whole rain systems. After first determining the location error, the total error for the rain
system can be decomposed into components due to location, intensity, and pattern errors.
Their relative magnitudes give clues as to the sources of forecast errors, whether they are
likely due to dynamical errors (incorrect location) or errors in physical processes
(intensity and pattern errors). Forecast rain systems can also be classified as "hits", "near
hits", "near misses", "misses" and "false alarms" according to their performance, which is
a useful and intuitive interpretation for forecasters.
Neighborhood (a.k.a. "fuzzy") verification methods give credit for rain forecasts that are
skilful on slightly larger scales, but do not precisely match the grid-scale spatial
distribution of rain. By verifying the forecast rain aggregated over increasingly large
neighborhoods (grid scale, 3x3 grid boxes, 5x5 grid boxes, etc.) it is possible to diagnose
the scale at which the model has sufficient skill to be considered useful. It also allows
models at different resolutions to be compared on a common spatial scale. Ebert (2008)
combined a large number of neighborhood verification methods from the published
literature into a single framework that provides a variety of scale-dependent information
on model performance.
NCMRWF has experience in verifying quantitative precipitation forecasts using
traditional verification approaches (Ashrit et al. 2009; Iyengar et al., 2010; Ashrit 2012).
We will assist with implementing the CRA method and the neighborhood verification
framework at NCMRWF and applying them to the UM monsoon rainfall forecasts to
provide additional diagnostic information on model performance. Observational data will
include rain gauge measurement and analyses over land, and TRMM-based multi-satellite
precipitation estimates (over the sea). Merged satellite-gauge gridded data (Mitra et al.,
2009) will also be used as observation.
2.3 Numerical investigation of UM
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Following detailed objective verification of baseline UM forecasts of the monsoon and
associated rainfall, identification of systematic errors in the forecasts, and subjective
evaluation of potential sources of deficiencies in the forecasts, a set of numerical
experiments will be designed to explore sensitivities to (i) model resolution, (ii)
initialization for prediction of heavy rain events, and (iii) model parameterizations. This
experimentation will focus on specific events that were high impact or poorly forecast.
Depending on the nature of the systematic errors, it may involve complete re-runs of
whole monsoon seasons. As part of the numerical experimentation, it would also be
valuable to revise existing or develop new conceptual models of monsoon rain systems
based on detailed diagnostics of monsoon structure and structure change. This would
provide under-pinning diagnostics for ongoing evaluation, verification, initialization and
prediction.
2.4 Ensemble prediction using MOGREPS
The MOGREPS ensemble prediction system based on the UM is designed to provide
advance prediction of high impact weather with quantitative estimates of its associated
uncertainty (Bowler et al. 2008). It consists of a 24-member global ensemble that runs to
five days, within which is nested a 24-member finer resolution regional ensemble that
predicts out to three days. Its spatial resolution is lower than that of the deterministic
model to enable multiple runs to be completed in a timely manner. To represent
uncertainties in the initial atmospheric state, perturbations to the initial conditions are
calculated using an ensemble transform Kalman filter. Model uncertainties are accounted
for using stochastic physics, which are random perturbations to the models' internal
tendencies.
CAWCR has implemented MOGREPS in experimental mode. Known locally as
AGREPS, this ensemble system has been running semi-routinely since 2011. Its spatial
resolution is N144 (~80km), currently being upgraded to N216 (~60km) for the global
system and 37.5 km, being upgraded to ~20km for the regional system located over a
large domain approximately centred over Australia. When a tropical cyclone is forecast, a
tracking algorithm is applied to identify the position of the cyclone in each ensemble
member, from which maps of strike probability and cyclone tracks can be generated.
3. Statement of Work (methodology to be adopted)
CAWCR will provide
(a) Expertise and code for diagnostic verification of gridded rainfall forecasts
(b) Modelling and data assimilation expertise
(c) Experience using cloud and rainfall profiles from TRMM, CloudSat and
CALIPSO
(d) Use of the Australian UM modelling infrastructure (ACCESS)
(e) Supercomputing resources
(f) Rain gauge observations and gridded analyses over Australia
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(g) Output from ACCESS global model and AGREPS ensemble
(h) Logistical support for visits of NCMRWF research staff to CAWCR
NCMRWF will provide
(a) Expertise in tropical meteorology
(b) Experience in model verification
(c) Research scientists to conduct the majority of the research
(d) Use of the Indian UM modelling infrastructure
(e) Supercomputing resources
(f) Rain gauge observations and gridded analyses over India
(g) Output from UM global and regional model, and MOGREPS ensemble if
implemented in time for this project, and MOGREPS ensemble if implemented in
time for this project
(h) Logistical support for visits of CAWCR research staff to NCMRWF
CAWCR's manpower contribution from PI Ebert and Co-PIs Davidson and Puri will
include time spent collaborating on the project in CAWCR, and visits to NCMRWF. We
understand that NCMRWF funding for participation in this project will be drawn from
another source.
The work package is described below.
Component 1. Comprehensively evaluate UM global and regional model forecasts of
rainfall in the monsoon and embedded weather using traditional and spatial
verification approaches to diagnose the nature and sources of model error.
Tasks:
(a) Implementation of spatial diagnostic verification methods (CRA and neighborhood
techniques) at NCMRWF.
(b) Traditional and spatial verification of model quantitative precipitation forecasts
against gridded rainfall from gauges (land) and satellite estimates from TRMM and
Megha-Tropiques (sea), for monsoon seasons during 2011-12 (Australia) and 2012
(India).
(c) Analysis of verification results with respect to rain intensity, model lead time, and
synoptic type and scale (synoptic, sub-synoptic, mesoscale, orographic).
(d) Interpretation of verification results vis-à-vis model processes in need of
improvement and utility of model QPFs in forecast office for short- and mediumrange prediction.
Deliverables: Diagnostic verification methods working at NCMRWF. Dataset of
verification statistics for global model predictions of tropical heavy rainfall for one wet
season each in India and Australia. Journal publication and conference presentation(s) on
performance of tropical heavy rainfall predictions from global and regional models.
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Component 2. Perform numerical experiments on selected rain events from
Component 1 to document the sensitivity of rainfall prediction to (a) system
resolution, (b) physical parameterizations of moist processes, surface properties (sea
surface temperature, soil moisture), boundary layer exchange and radiation.
Tasks:
(a) Identification of a small number of cases of observed and forecast monsoon rainfall
in India, including tropical cyclones and monsoon depressions.
(b) UM simulations for these cases, varying the model grid spacing and physical
parameterization settings to test model sensitivity.
(c) Quantitative assessment of model rainfall output using verification methods
implemented in Component 1.
(d) Assessment of modelled vertical rain and cloud profiles against space-based rain and
cloud profile data from TRMM, CloudSat and CALIPSO.
(e) Identification of model configuration and settings giving the most realistic
simulations for these cases.
Deliverables: Dataset of UM model rainfall spatial fields and vertical profiles, observed
rainfall analyses and vertical profiles, and verification statistics for tropical heavy rain
cases in India. Optimum UM model configuration and settings.
Component 3. Using the MOGREPS ensemble, investigate the predictability of
monsoon heavy rain events, and assess the potential of ensembles to provide
advance warning and probabilistic predictions for risk assessment and decision
making.
Tasks:
(a) Generation of probabilistic rain forecasts from MOGREPS ensemble output for lead
times out to 5 days for monsoon seasons in Component 1.
(b) Verification of ensemble spread and probabilistic quantitative precipitation forecasts
using standard methods and object-based ensemble verification.
(c) Evaluation of MOGREPS ensemble forecasts for rain cases in Component 2.
(d) Interpretation of results in terms of probabilistic skill and potential usefulness in
advance warning context.
Deliverables: Dataset of ensemble rainfall output and verification statistics for Indian and
Australian monsoon seasons and selected tropical heavy rain cases in India. Journal
publication and conference presentation(s) on performance of ensemble-based forecasts
for tropical heavy rainfall.
3.1 Schedule (Year wise)
Year
Expected Outcome
Deliverables
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Year – 1
UM rainfall performance
benchmarked for two wet
seasons in India and Australia
using traditional and diagnostic
spatial verification.
Diagnostic verification methods working
at NCMRWF.
Dataset of verification statistics for global
model predictions of tropical heavy
rainfall for one wet season each in India
and Australia.
Interaction with forecasters on
UM tropical rainfall quality
(give/receive feedback).
Dataset of UM model rainfall spatial
fields and vertical profiles, observed
rainfall analyses and vertical profiles, for
tropical heavy rain cases in India.
Preparation of observations and
model rain data for selected
rainfall cases.
Documentation of results.
Year – 2
Year – 3
Numerical experimentation with
UM to determine model
sensitivity to resolution,
parameterizations, initialisation.
Journal publication and conference
presentation(s) on performance of tropical
heavy rainfall predictions from global and
regional models.
Probabilistic rainfall forecasts
generated from MOGREPS
ensemble.
Dataset of verification results for selected
Indian tropical heavy rain cases.
Further numerical
experimentation to determine
optimal model configuration.
Optimum UM model configuration and
settings.
Dataset of ensemble rainfall output and
verification statistics for Indian and
Australian monsoon seasons and selected
Indian tropical heavy rain cases.
Verification of MOGREPS
ensemble forecasts.
Documentation of results.
Journal publication and conference
presentation(s) on performance of
ensemble-based forecasts for tropical
heavy rainfall.
3.2 Team composition and expertise *
Investigator
PI (E. Ebert)
Co-PI (1) (N. Davidson)
Co-PI (2) (K. Puri)
Investigator (G. R. Iyengar)
Investigator (R. Ashrit)
Post-doc (to be named)
Qualification
PhD, Meteorology
PhD, Mathematical
Sciences
PhD, Physics
MSc, Physics
PhD, Atmospheric Sciences
PhD
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Expertise
Forecast verification
Tropical meteorology
Numerical modelling
Forecast verification
Forecast verification
*MoES encourages to include one Indian partner in the project proposal, preferably
from MoES institutes.
3.3 Connections to Operational forecast and Human resource development
Improvements to NWP guidance will translate directly to better operational forecasts.
Quantifying and communicating the errors in UM predictions of monsoon rainfall, in
particular the nature of systematic errors and the magnitude of non-systematic errors, will
enable forecasters to interpret the model output in a more informed way, also leading to
better operational forecasts. We anticipate that MOGREPS ensemble forecasts will
extend the useful lead time for medium-range prediction of synoptic scale events like
monsoon depressions and tropical cyclones; this project will quantify the expected
benefit of scenario and probabilistic forecasts derived from ensembles.
This project will contribute to the development of scientific skills of participating
scientists in NCMRWF and CAWCR in the areas of forecast verification, numerical
modelling, and tropical meteorology. In particular, NCMRWF scientists will build
expertise in advanced diagnostic verification methods. NCMRWF has identified two
project scientists whom they plan to groom as modelling and verification experts; this
project would further their development. Working relationships developed during this
collaboration may lead to future research opportunities for all concerned.
4. Related works and project assessment :
4.1 National status
4.1.1 ACCESS model development
Development of ACCESS has progressed along the lines laid out in the recommendations
made in the Blueprint for ACCESS (Puri, 2005) and Project Plan for ACCESS (Puri,
2005). Significant progress has been made since development started in 2006 and some
of the key milestones include:


Operational implementation of the ACCESS NWP system including assimilation of
significantly increased number of satellite sounders. Implementation in September
2009 has been marked by a significantly increased skill relative to the previous
operational systems, and the system performance is now similar to other major
operational centres.
Successful assembly of fully coupled ACCESS and detailed testing of the coupled
system. Subsequently the core CMIP5 simulations have been completed with two
configurations of ACCESS, one using the Met Office MOSES land surface and a
second one using CABLE and upgraded physical parametrizations. Model output
data from both configurations has been published on the Earth System Grid (ESG)
and will form an important component of the Australian contribution to CMIP5 and
the IPCC Fifth Assessment. Preliminary assessment of the ACCESS runs shows
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



encouraging performance of the system with ACCESS falling in the top tier of the
climate models.
Development of high resolution (1.5km) version for severe weather prediction,
including radar data assimilation which has shown encouraging skill in predicting
heavy rainfall events.
Implementation in research mode of the ACCESS ensemble prediction system
(known as AGREPS).
Commencement of work on using the ACCESS coupled system for seasonal
prediction.
Progress in the development of ACCESS infrastructure. Examples include successful
porting to both Bureau and National Computational Infrastructure’s (NCI)
supercomputers; development, in collaboration with the Centre of Excellence for
Climate System Science, of infrastructure to enable ready usage by University
researchers; and setting up of a unified repository based at NCI.
The ACCESS NWP implementation and evaluation of its performance has been reported
in Puri et al. (2012) and a number of manuscripts describing the assembly of the fully
coupled ACCESS and evaluation of the CMIP5/AR5 runs have been submitted for
publication (Dix et al., 2012, Bi et al., 2012, Sun et al., 2012).
4.1.2 ACCESS-TC for operations and research
The Australian Community Climate and Earth System Simulator, ACCESS (Puri et al.
2012) has been configured for operational and research applications on Tropical
Cyclones. The base system runs at a resolution of 0.11° and 50 levels. The domain is relocatable and nested in coarser-resolution forecasts. It provides guidance for storms
located within the approximate domain: 40°S to 40°N, 70°E to 180°E. Initialization
consists of 5 cycles of 4D-VAR assimilation over 24 hours and forecasts to 72 hours are
made. Using all available conventional observations and only synthetic surface pressure
observations from an idealized vortex to correct the initial location and structure of the
storm, the 4DVAR builds a balanced, intense 3-D vortex with a well-developed
secondary circulation and maximum wind at the radius of maximum wind. Mean forecast
track and intensity errors for Australian region and NW Pacific storms have been
encouraging. The system became fully operational at the Australian National
Meteorological and Oceanographic Centre in November 2011 (NMOC, 2011). The
system has been used effectively to simulate a small number of heavy rain events over
Australia.
4.1.3 Australian Climate Change Science Program project on extreme rain events
Amplifying Planetary Rossby Waves and Rain Processes during Extreme Weather
Events in Current and Future Climates
The objectives of this project are to:
1. Develop a climatology of extreme rain events over Australia.
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2. Develop a classification of extreme rain environments.
3. Use an index of Rossby wave activity to characterize the mid-latitude circulation over
Australia.
4. Document the variability in Rossby wave activity and its association with extreme rain
events over Australia.
5. Validate Rossby wave activity and extreme rain events in current and future climates
based on CMIP5 and/or available climate simulations.
4.2 International status
4.2.1 US Office of Naval Research (ONR) Award: Rapid Intensification of TCs
Initialization, Prediction and Diagnosis of the Rapid Intensification of Tropical
Cyclones using the Australian Community Climate and Earth System Simulator,
ACCESS
This is a four-part, interconnected program of
(a) basic research into initialization of realistic TC structures using the state-of-the-art 4dimensional variational data assimilation system (4D-VAR) from ACCESS (Australian
Community Climate and Earth System Simulator)
(b) very high-resolution forecast experiments on prediction of TC structure and intensity,
with particular focus on Rapid Intensification, using ACCESS
(c) diagnosis of the mechanisms of TC intensity and structure change (environmental
influences, vortex structure, internal processes),
(d) transitioning of a validated TC assimilation and prediction system into operations, to
provide forecast guidance on TC track, intensity and structure change.
4.2.2 International High Ice Water Content Study
The International High IWC Study will address engineering and scientific issues related
to the failure of jet engines commonly used on commercial aircraft in convective clouds,
and a variety of fundamental scientific issues related to the microphysical properties and
structure of deep convective cloud systems over land and over the warm tropical ocean.
Scientific partners include NASA, the European Union High Altitude Ice Crystal (HAIC)
consortium, JMA Meteorological Research Institute, Environment Canada, and NCAR.
CAWCR will contribute operationally (through the Darwin Climate Research Facility,
and the operational BOM observations and forecasts) and scientifically (as members of
the International Science Team and the HAIC consortium). An international field
experiment out of Darwin, Australia is planned in January-March 2014.
CAWCR's main objectives in this project are to:
 Analyze existing airborne datasets with high IWC and contribute in HAIC to the
definition of the payload of the Falcon 20.
14





Collect the ground-based and airborne dataset for the characterization of the
microphysical properties of high IWC regions, and more generally of tropical
oceanic convective systems,
Analyze the ground-based and airborne dataset in order to build a comprehensive
picture of the formation and maintenance of high IWC regions in the upper
troposphere,
Evaluate and improve ground-based, airborne, and satellite retrieval methods for the
characterization of the dynamical and microphysical properties of high IWC regions,
Characterize the frequency of occurrence of high IWC regions at global scale using
the newly-developed satellite methods.
Use the knowledge gained from this analysis to evaluate cloud processes within the
ACCESS model and inform improvements to the model physics and dynamics.
4.3 The mechanisms adopted in your institute for internal review (assessment) and
validation of this Project Proposal.
This proposal initiated by CAWCR scientists was developed through consultation with
the partner investigators at NCMRWF to develop the research ideas and define the goals
and deliverables. Discussion with the CAWCR Director and Deputy Director to confirm
alignment of the project to CAWCR research priorities was also part of the process. After
a final draft of the proposal was prepared, a letter of support was obtained from the
CAWCR Deputy Director.
A review of potential legal issues and project costings by CAWCR's Business
Development and Finance officers was undertaken as well. Staffing costs have been
estimated by the Finance officer. Costs associated with proposed travel and journal page
charges were estimated from experience and online airline and hotel information. These
are indicative at best.
5. Results from prior MoES support (if any)
None
6. Facilities available at the workspace
CAWCR scientists have access to two supercomputer facilities, namely the Bureau’s
4608 core Oracle/Sun constellation system delivering 50 Tflops peak and the National
Computational Infrastructure (based at the Australian National University in Canberra)
11936 core Oracle/Sun constellation system delivering 150 Tflops peak. The NCI system
will be upgraded to a 1.2 petaflop Fujitsu PRIMERGY system in the first quarter 2013.
Mid-range computing on a high performance research and development Sun cluster (27
nodes, 53 CPUs, 216 cores, 680GB RAM, 32 TB storage) running a large number of
applications software packages (IDL, MatLab, NetCDF tools, etc.) is available for
analysis of results and other jobs not requiring supercomputing.
NCMRWF has a IBM-P6 based High Performance Computing System having a peak
performance of 24 TFlops and has plans to upgrade the existing computing resources.
15
7. Total Budget ( $ ) requirements ** (with justifications)
Item Name
S.No
A)
1st Year
2nd Year
3rd Year
Total
Justification
Man Power
1) Key personnel
45,000
46,000
47,000
138,000 Staff costs for Ebert, Davidson, Puri @ 0.05
FTE each, for three years
45,000
46,000
47,000
138,000
1) Foreign Travel
6,000
6,000
6,000
Total budget for Travel
6,000
6,000
6,000
51,000
52,000
53,000
2) Other personnel (e.g.
Research Assistants)
3) Technical Assistant
Total budget for
Manpower
B)
Travel
Grand Total (for each year)
18,000 One two-week visit of CAWCR scientist to
NCMRWF each year (flight, accommodation,
food and incidentals)
18,000
156,000
** As per MoES guidelines, we will not be paying any additional overheads for international proposals. Funding is available only for
manpower (not exceeding $1, 00,000/- per year/per person) and travel.
16
8. Bio-data (CV) of the Investigators :
8.1 PI Biography (Dr. Elizabeth Ebert)
Name:Dr. Elizabeth Ebert
Date of birth:- 24 February 1960
Institution:- CAWCR, Bureau of Meteorology
Address (Residence):- 46 Henry Street, Windsor, Victoria 3181, AUSTRALIA
Tel. No: +61-3-9510-5293
Mob No: +61-406-763-248
E-mail Id: e.ebert@bom.gov.au
Address (Office):- 700 Collins Street, Docklands, Victoria 3008, AUSTRALIA
Tel. No: +61-3-9669-4688
FAX: +61-3-9669-4660
Official E-mail Id: e.ebert@bom.gov.au
Official website address: http://cawcr.gov.au/, http://cawcr.gov.au/staff/eee/
Educational Qualification:School/College/University Degree
Year
Main subjects
University of California
B.S.
1981
University of Wisconsin
University of Wisconsin
M.S.
Ph.D.
1984
1987
Atmospheric
Science
Meteorology
Meteorology
Division/Class
Awards / Honors / Fellowships, etc.:
Bureau of Meteorology Award for Individual Excellence, 12 March 2008
WMO recognition of contribution toward initiation and development of validation
efforts within the International Precipitation Working Group, 3rd IPWG Workshop,
23-27 October 2006, Melbourne.
AMS Committee on Probability and Statistics, 2007-2009
THORPEX GIFS/TIGGE Working Group, 2005-2011
GPM Ground Validation Steering Committee, 2003-present
WWRP/WGNE Joint Working Group on Forecast Verification Research, 2003-present;
co-chair 2008-present
WWRP Beijing 2008 FDP Team, 2005-2009
International Precipitation Working Group (IPWG), 2002-present
WWRP Sydney 2000 FDP Verification Team, 2001-2002
Global Precipitation Climatology Project (GPCP) Science Advisory Team, 1998-2001
Australian Academy of Science Atmospheric and Oceanic Sciences Committee, 19972000
Associate editor, Monthly Weather Review, 2006-2008
Associate editor, Weather and Forecasting, 2004-2007
18
Appointments (Professional experience/employment record):
Organization
Designation / Position
Bureau of Meteorology
Research Centre
Bureau of Meteorology
Research Centre
Bureau of Meteorology,
Centre for Australian Weather
& Climate Research
Bureau of Meteorology,
Centre for Australian Weather
& Climate Research
Professional Officer
Duration (Year
/ date)
1989-1995
Senior Professional Officer
1995-2007
Research Team Leader, NWP
Applications Team
2007-2009
Research Program Leader,
Weather and Environmental
Prediction Program
2009-present
Ten most relevant research publications: (most recent first)
Brown, B.G., E. Gilleland and E.E. Ebert, 2011: Forecasts of spatial fields. In I.T.
Jolliffe and D.B. Stephenson (eds), Forecast Verification: A Practitioner's Guide
in Atmospheric Science. 2nd ed., Wiley.
Ebert, E.E., M. Turk, S.J. Kusselson, J. Yang, M. Seybold, P.R. Keehn, R.J.
Kuligowski, 2011: Ensemble tropical rainfall potential (eTRaP) forecasts. Wea.
Forecasting, 26, 213-224.
Bougeault, P., Z. Toth, C. Bishop, B. Brown, D. Burridge, D.H. Chen, B. Ebert, M.
Fuentes, Tom Hamill, K. Mylne, J. Nicolau, T. Paccagnella, Y.-Y. Park, D.
Parsons, B. Raoult, D. Schuster, P. Silva Dias, R. Swinbank, Y. Takeuchi, W.
Tennant, L. Wilson and S. Worley, 2010: The THORPEX Interactive Grand
Global Ensemble (TIGGE). Bull. Amer. Met. Soc., 8, 1059-1072.
Gilleland, E., D.A. Ahijevych, B.G. Brown and E.E. Ebert, 2010: Verifying
forecasts spatially. Bull. Amer. Met. Soc., 91, 1365-1373.
Ebert, E.E., 2008: Fuzzy verification of high resolution gridded forecasts: A review
and proposed framework. Meteorol. Appl., 15, 51-64.
Ebert, E.E., J.E. Janowiak, and C. Kidd, 2007: Comparison of near real time
precipitation estimates from satellite observations and numerical models. Bull.
Amer. Met. Soc., 88, 47-64.
Ebert, E.E., U. Damrath, W. Wergen and M.E. Baldwin, 2003: The WGNE
assessment of short-term quantitative precipitation forecasts. Bull. Amer. Met.
Soc., 84, 481-492.
Ebert, E.E., 2001: Ability of a poor man's ensemble to predict the probability and
distribution of precipitation. Mon. Wea. Rev., 129, 2461-2480.
Ebert, E.E. and J.L. McBride, 2000: Verification of precipitation in weather
systems: Determination of systematic errors. J. Hydrology, 239, 179-202.
McBride, J.L. and E.E. Ebert, 1999: Verification of quantitative precipitation
forecasts from operational numerical weather prediction models over Australia.
Wea. Forecasting, 15, 103-121.
19
Other publications:
De Maria, E.M.C., D.A. Rodriguez, E.E. Ebert, F. Su, and J.B. Valdes, 2011:
Evaluation of mesoscale convective systems in South America using multiple
satellite products and an object-based approach. J. Geophys. Res., 116,
D08103, doi:10.1029/2010JD015157.
Schuster, G., E.E. Ebert, M.A. Stevenson, R.J. Corner, C.A. Johansen, 2011:
Application of satellite precipitation data to analyse and model relationships
between Murray Valley encephalitis virus and rainfall in Western Australia. Int. J.
Health Geographics, 10:8 doi:10.1186/1476-072X-10-8.
Dance, S., E. Ebert and D. Scurrah, 2010: Thunderstorm strike probability
nowcasting. J. Atmos. Oceanic. Tech., 27, 79-93.
Ahijevych, D., E. Gilleland, B. Brown, and E. Ebert, 2009: Application of spatial
verification methods to gridded precipitation forecasts. Weather and Forecasting,
24, 1485–1497.
Ebert, E.E., 2009: Neighborhood verification of high resolution precipitation
products. In Hossain, F. and M. Gebremichael (eds), Satellite Rainfall
Applications for Surface Hydrology. Springer.
Ebert, E.E., 2009: Neighborhood verification of high resolution precipitation
forecasts. Weather and Forecasting, 24, 1498-1510.
Ebert, E.E. and W.A. Gallus, 2009: Toward better understanding of the contiguous
rain area (CRA) verification method. Weather and Forecasting, 24, 1401-1415.
Gilleland, E., D. Ahijevych, B.G. Brown, B. Casati, and E.E. Ebert, 2009: Intercomparison of spatial verification methods. Weather and Forecasting, 24, 1416–
1430.
Turk, F.J., B.-J. Sohn, J.-J. Oh, E.E. Ebert, V. Levizzani, and E.A. Smith, 2009:
Validating a rapid-update satellite precipitation analysis across telescoping space
and time scales. Meteorol. Atm. Phys., 105, 99-108.
Casati, B., L.J. Wilson, D.B. Stephenson, P. Nurmi, A. Ghelli, M. Pocernich, U.
Damrath, E.E. Ebert, B.G. Brown and S. Mason, 2008: Forecast verification:
current status and future directions. Meteorol. Appl., 15, 3-18.
Rossa, A., P. Nurmi, and E. Ebert, 2008: Overview of methods for the verification
of quantitative precipitation forecasts. In Michaelides, S. (ed), Precipitation:
Advances in Measurement, Estimation and Prediction, pp. 417-450.
Turk, F.J., P.Arkin, E. Ebert and M. Sapiano, 2008: The First Workshop of the
Program for the Evaluation of High Resolution Precipitation Products. Bull. Amer.
Met. Soc., 89, 1911-1916.
Engel, C. and E. Ebert, 2007: Performance of hourly operational consensus
forecasts (OCFs) in the Australian region. Weather and Forecasting, 22, 13451359.
20
Ebert, E.E., 2006: Methods for verifying satellite precipitation estimates. In
Levizzani, V., P. Bauer and F.J. Turk (eds), Measuring Precipitation from Space.
EURAINSAT and the Future.
Grams, J.S., W.A. Gallus, L.S. Wharton, S. Koch, A. Loughe, and E.E. Ebert, 2006:
The use of a modified Ebert-McBride technique to evaluate mesoscale model QPF
as a function of convective system morphology during IHOP 2002. Weather and
Forecasting, 21, 288–306.
Ebert, E., S. Kusselson and M. Turk, 2005: Validation of NESDIS operational
tropical rainfall potential (TRaP) forecasts for Australian tropical cyclones. Aust.
Meteorol. Mag., 54, 121-135.
Ebert, E., L.J. Wilson, B.G. Brown, P. Nurmi, H.E. Brooks, J. Bally, and M.
Jaeneke, 2004: Verification of nowcasts from the WWRP Sydney 2000 Forecast
Demonstration Project. Weather and Forecasting, 19, 73-96.
May, P.T., T. Keenan, R. Potts, R. Webb, A. Treloar, E. Spark, S. Lawrence, E.
Ebert, J. Bally, J. Wilson, and P. Joe, 2004: The Sydney observing network
during the Sydney 2000 Olympic Games Forecast Demonstration Project:
Addressing forecast issues and nowcasting requirements. Weather and
Forecasting, 19, 115-130.
Pierce, C.E., E.E. Ebert, A. Seed, N. Fox, M. Sleigh, C.G. Collier, N. Donaldson, J.
Wilson, R. Roberts and C. Mueller, 2004: The nowcasting of precipitation during
Sydney 2000: An appraisal of the QPF algorithms. Weather and Forecasting, 19,
7-21.
Wilson, J.W., E. Ebert, T. Saxen, C. Pierce, M. Sleigh, A. Seed, R. Roberts and C.
Mueller, 2004: Sydney 2000 Forecast Demonstration Project: Convective storm
nowcasting. Weather and Forecasting, 19, 131-150.
Keenan, T., P. Joe, J. Wilson, C. Collier, B. Golding, D. Burgess, P. May, C. Pierce,
J. Bally, A. Crook, D. Sills, L. Berry, I. Bell, N. Fox, R. Pielke Jr., E. Ebert, M.
Eilts, K. O'Loughlin, R. Webb, R. Carbone, K. Browning, R. Roberts, C. Mueller,
2002: The Sydney 2000 World Weather Research Programme Forecast
Demonstration Project: Overview and current status. Bull. Amer. Met. Soc., 84,
1041–1054.
Ebert, E.E. and G.T. Weymouth, 1999: Incorporating satellite observations of "no
rain" into an Australian daily rainfall analysis. J. Appl. Meteor., 38, 44-56.
Ebert, E.E. and M.J. Manton, 1998: Performance of satellite rainfall estimation
algorithms during TOGA COARE. J. Atmos. Sci., 55, 1537-1557.
Weymouth, G., G.A. Mills, D. Jones, E.E. Ebert, and M.J. Manton, 1998: A
continental-scale daily rainfall analysis system. Aust. Meteorol. Mag., 48, 169179.
Schramm, J.L., M.M. Holland, J.A. Curry and E.E. Ebert, 1997: Modeling the
thermodynamics of a sea ice thickness distribution. Part I: Sensitivity to ice
thickness resolution. J. Geophys. Res., 102, 23,079-23,092.
21
Ebert, E.E., M.J. Manton, P.A. Arkin, R.J. Allan, G.E. Holpin, and A.J. Gruber,
1996: Results from the GPCP Algorithm Intercomparison Programme, Bull.
Amer. Met. Soc., 77, 2875-2887.
Curry, J.A., J.L. Schramm, M.C. Serreze and E.E. Ebert, 1995: Water vapor
feedback over the Arctic Ocean, J. Geophys. Res., 100, 14,223-14,229.
Curry, J.A., J.L. Schramm and E.E. Ebert, 1995: Sea ice-albedo climate feedback
mechanism, J. Climate, 8, 240-247.
Ebert, E.E. and J. Le Marshall, 1995: An evaluation of infrared satellite rainfall
estimation techniques over Australia, Aust. Meteorol. Mag., 44, 177-190.
Ebert, E.E., J.L. Schramm and J.A. Curry, 1995: Disposition of solar radiation in
sea ice and the upper ocean. J. Geophys. Res., 100, 15,965-15,975.
Curry, J.A., E.E. Ebert and J.L. Schramm, 1993: Impact of clouds on the surface
radiation balance of the Arctic Ocean, Meteorol. Atmos. Phys., 51, 197-217.
Ebert, E.E. and J.A. Curry, 1993: An intermediate one-dimensional thermodynamic
sea ice model for investigating ice-atmosphere interactions, J. Geophys. Res., 98,
10,085-10,109.
Curry, J.A. and E.E. Ebert, 1992: Annual cycle of radiation fluxes over the Arctic
Ocean: Sensitivity to cloud optical properties, J. Climate, 5, 1267-1280.
Ebert, E.E. and J.A. Curry, 1992: A parameterization of ice cloud optical properties
for climate models. J. Geophys. Res., 97, 3831-3836.
Ebert, E.E. and G.J. Holland, 1992: Observations of record cold cloud top
temperatures in tropical cyclone Hilda (1990). Mon. Wea. Rev, 120, 2240-2251.
Ebert, E.E., 1991: Pattern recognition analysis of polar clouds during summer and
winter. Int. J. Remote Sensing, 13, 97-109.
Curry, J.A. and E.E. Ebert, 1990: Sensitivity of the thickness of Arctic sea ice to the
optical properties of clouds. Annals of Glaciology, 14, 43-46.
Curry, J.A., F.G. Meyer, L.F. Radke, C.A. Brock, and E.E. Ebert, 1990: Occurrence
and characteristics of lower tropospheric ice crystals in the Arctic. Int. J.
Climatol., 10, 749-764.
Ebert, E.E., 1989: Analysis of polar clouds from satellite imagery using pattern
recognition with a statistical cloud analysis scheme. J. Appl. Meteor., 28, 382399.
Curry, J.A., G.F. Herman and E.E. Ebert, 1988: Mean and turbulence structure of
the summertime Arctic cloudy boundary layer. Q. J. R. Meteorol. Soc., 114, 715746.
Ebert, E.E., U. Schumann and R.B. Stull, 1988: Nonlocal turbulent mixing in the
convective boundary layer evaluated from large-eddy simulation. J. Atmos. Sci.,
46, 2178-2207.
Eloranta, E.W., R. B. Stull and E. Ebert, 1988: Test of a calibration device for
airborne Lyman-alpha hygrometers. J. Atmos. Ocean Tech., 6, 129-139.
22
Ebert, E.E., 1987: A pattern recognition technique for distinguishing surface and
cloud types in the polar regions. J. Clim. Appl. Meteor., 26, 1412-1427.
Participation in Conference/Seminar/Workshop/Summer Schools
Chair, 5th International Verification Methods Workshop, 1-7 December 2011,
Melbourne, Australia.
Trainer at 14 WMO training courses and Asia-Pacific Satellite Applications courses
Invited speaker at 20 international workshops and conferences
Presenter or participant at many other workshops and conferences
Recent collaborations:
Intercomparison of satellite precipitation estimates (with NASA, NOAA, NRL, UCI,
JAXA; 2000-present).
Spatial Verification Methods Intercomparison Project (with NCAR, UK Met Office,
universities; 2007-2009)
WWRP Beijing 2008 Olympics Forecast Demonstration Project (with CMA, NCAR,
Env. Canada, HKO)
Research Guidance:
No. of Ph.D. students enrolled/ completed: 3 completed, 1 current
Synergistic Activities:
Operational implementation in the Bureau
Poor Man's Ensemble (PME) of NWP rainfall predictions, 2006
RAINVAL, a tool for automated and interactive verification of gridded quantitative
precipitation forecasts, 2000
Operational implementation outside Bureau
Real Time Forecast Verification (RTFV) system implemented at Beijing
Meteorological Bureau for use in WWRP 2008 Beijing Olympics Forecast
Demonstration Project, July 2008.
Ensemble TRaP (eTRaP) implemented at NOAA/NESDIS Satellite Analysis Branch,
May 2008.
Cyclone XML (CXML) v.1.0 released for TIGGE exchange of ensemble tropical
cyclone tracks, 4 June 2008.
Weather XML (WxML) v.1.0 released for exchange of nowcasts as part of the
WWRP 2008 Beijing Olympics Forecast Demonstration Project, August 2006.
23
8.2 Co-PI (1) Biography (Dr. Noel Davidson)
Name:
Noel E. Davidson
Date of birth:
10 December 1948
Institution:
CAWCR, Bureau of Meteorology
Address (Residence): 8 Pryde Court, Wheelers Hill, Melbourne, Australia. 3150
Tel. No. : +61-3-9561-2864
Mob No:
E-mail Id: n.davidson@bom.gov.au
Address (Office):-
700 Collins Street, Docklands, Victoria 3008, AUSTRALIA
Tel. No: +61-3-9669-4416
FAX: +61-3-9669-4660
Official E-mail Id: n.davidson@bom.gov.au
Official website address: http://cawcr.gov.au/
Educational Qualification:
School/College/University Degree
Monash University
B.Sc.
(Hons)
Year
1970
Monash University
1995
PhD
Main subjects
Astrophysics,
Atmospheric
Science
The Monsoon
and Tropical
Cyclones
Division/Class
Awards / Honors / Fellowship etc.:
1. Lead Principal Investigator, US National Oceanographic Partnership Program (NOPP) and
the US Office of Naval Research (ONR) Award (2010 – 2012):
“Initialization, Prediction and Diagnosis of the Rapid Intensification of Tropical
Cyclones, using the Australian Community Climate and Earth System Simulator,
ACCESS”
Co-Principal Investigators: Michael J. Reeder, Craig H. Bishop, Jeffrey D. Kepert, Peter
Steinle and Kevin J. Tory.
2. Lead Principal Investigator, 2010-2012 Australian Climate Change Science Program
(ACCSP) Proposal:
“Amplifying Planetary Rossby Waves and Rain Processes during High Impact Weather
Events in Current and Future Climates”
Co-Principal Investigators: Hongyan Zhu, Harry Hendon and Michael J. Reeder.
3. Lead Principal Investigator, US Office of Naval Research (ONR) Project (2002-2005):
"Research and Development Towards Improved Operational Prediction of Tropical
Cyclone Behavior and Associated Significant Weather"
24
Member Australian Meteorological and Oceanographic Society
Member American Meteorological Society (AMS)
Member AMS Committee on Tropical Meteorology and Hurricanes, 2000 - 2002
Member Editorial Board of the Australian Meteorological Magazine, Associate Editor,
1999-2005.
Visiting Research Scientist, Meteorological Research Institute, Japan Meteorological
Agency, Tsukuba, Japan.
Visiting Research Scientist, Supercomputer Computations Research Institute and the
Department of Meteorology, Florida State University, with Prof. T.N. Krishnamurti.
Leader, IWTC Working Group on Tropical Cyclone Prediction, 2002.
Recipient of the Priestley Award, (1982) from the Australian Meteorological and
Oceanographic Society, for excellence in meteorological or oceanographic research,
for Davidson, N.E. and B.J. McAvaney, 1981: The ANMRC tropical analysis
scheme. Aust. Met. Mag., 29, 155-168.
Appointments (Professional experience/employment record):
Organization
Bureau of
Meteorology
Australian Numerical
Meteorology Research
Centre
Bureau of
Meteorology Research
Centre (BMRC)
BMRC
CAWCR
CAWCR
Designation / Position
Professional Officer
(Meteorologist)
Senior Professional Officer
(Research)
Duration ( Year / date)
1975-1977
Senior Professional Officer
(Research)
1985 - 1998
Senior Research Scientist
Senior Research Scientist
Principal Research Scientist
1998 – 2008
2008 - 2011
2011 - 2012
1978 - 1984
List of important and relevant research publications:
Davidson, N.E. and Y. Ma, 2012: Surface Pressure Profiles, Vortex Structure and
Initialization for Hurricane Prediction. Part II: Numerical Simulations of Track,
Structure and Intensity, Meteorol. Atmos. Phys, 117, 25-45.
Wang, X., Y. Ma and N.E. Davidson, 2012: Secondary eyewall formation and eyewall
replacement cycles in a simulated hurricane: Effect of unbalanced forces in the
atmospheric boundary layer, J.Atmos. Sci., under revision.
Nguyen, C.M., M.J. Reeder, N.E. Davidson, M.T. Montgomery and R.K. Smith, 2011:
Vacillation cycles during the intensification of Hurricane Katrina. Quart. J. Roy.
Met. Soc., 137, 829–844.
Tory, K. J., N. E. Davidson and M. T. Montgomery 2007: Prediction and diagnosis
of tropical cyclone formation in an NWP system. Part III: Developing and nondeveloping storms. J. Atmos. Sci. 64, 3195 – 3213.
25
Davidson, N.E., K.J. Tory, M. J. Reeder and W.L. Drosdowsky, 2007: Extratropicaltropical interaction during onset of the Australian Monsoon: Re-analysis
diagnostics and idealized dry simulations. J. Atmos. Sci., 64, 3475-3498.
Dare, R.A and N.E. Davidson, 2004: Characteristics of tropical cyclones in the
Australian Region. Mon. Wea. Rev., 132, 3049-3065.
Davidson, N.E. and S.K. Kar, 2002: Upper tropospheric flow transitions during rapid
tropical cyclone intensification. Quart. J. Roy. Met. Soc., 128, 861-891.
Davidson, N.E. and H.C. Weber, 2000: The BMRC high resolution tropical cyclone
prediction system: TC-LAPS. Mon. Wea. Rev., 128, 1245-1265
Davidson, N.E., K. Kurihara, T. Kato, G. Mills and K. Puri, 1998: Dynamics and
prediction of a mesoscale, extreme rain event in the Baiu front over Kyushu, Japan.
Mon. Wea. Rev. , 126, 1608-1629.
Davidson, N.E. and K. Puri, 1992: Tropical prediction using dynamical nudging,
satellite-defined convective heat sources and a cyclone bogus. Mon. Wea. Rev., 120,
2501-2522.
Davidson, N.E. and H.H. Hendon, 1989: Downstream development in the Southern
Hemisphere monsoon during FGGE/WMONEX. Mon. Wea. Rev., 117, 1458-1470.
Davidson, N.E. and G.J. Holland, 1987: A diagnostic analysis of two intense monsoon
depressions over northern Australia. Mon. Wea. Rev., 115, 380-392.
Davidson, N.E., J.L. McBride, and B.J. McAvaney, 1983: The onset of the Australian
monsoon during winter MONEX: Synoptic aspects. Mon. Wea. Rev., 111, 496-516.
Other publications:
Tory, K.J., R. A. Dare, N. E. Davidson, J. L. McBride, and S. S. Chand, 2012: The
importance of low-deformation vorticity in tropical cyclone formation, Atmos.
Chem.
Phys.
Discuss.,
12,
1–43,
2012,
www.atmos-chem-physdiscuss.net/12/1/2012/ doi:10.5194/acpd-12-1-2012
Davidson, N. E. and Y. Ma, 2012: Surface Pressure Profiles, Vortex Structure and
Initialization for Hurricane Prediction. Part II: Numerical Simulations of Track,
Structure and Intensity, Meteorol. Atmos. Phys, 117, 25-45.
Luo, Z., N.E. Davidson, F. Ping and W. Zhou, 2011: Multi-scale interactions
affecting tropical cyclone track changes. Adv. Mech. Eng.,
Davidson, N.E., 2010: On the intensification and recurvature of Tropical Cyclone
Tracy (1974). Austr. Meteorol. Oceanogr. J., 60, 169-177
Ma, L., J. C. Chan, N. E. Davidson and J. Turk, 2008: Initialization with diabatic
heating from satellite-derived rainfall. Atmospheric Research, 85,148-158.
Tory K. J., M. T. Montgomery, N. E. Davidson, and J. D. Kepert, 2006: Prediction
and diagnosis of tropical cyclone formation in an NWP system. Part II: A
diagnosis of Tropical Cyclone Chris formation. J. Atmos. Sci., 63, 3091–3113.
26
Tory, K. J., M. T. Montgomery and N. E. Davidson, 2006: Prediction and diagnosis
of tropical cyclone formation in an NWP system. Part I: The critical role of vortex
enhancement in deep convection. J. Atmos. Sci., 63, 3077 - 3090.
Paterson, L., B. Hanstrum, N.E. Davidson and H.C. Weber, 2005: Influence of
environmental wind shear on the intensity of hurricane-strength tropical cyclones
in the Australian region. Mon. Wea. Rev., 133, 3644-3660.
Davidson, N.E., H. Stern, G. Mills and T. Leggett, 2001: Forward-backward
assimilation, prediction and dynamics for the southeast Australia floods of October
1993. Aust. Met. Mag., 50, 205-224
Li, J., N.E. Davidson, D. Hess and G. Mills, 1997: A high resolution prediction study
of two typhoons at landfall. Mon. Wea. Rev. , 125, 2856-2878.
Davidson, N.E., 1995a: Vorticity Budget for AMEX. Part I : Diagnostics. Mon. Wea.
Rev., 123, 1620-1635.
Davidson, N.E., 1995b: Vorticity Budget for AMEX. Part II : Simulations of monsoon
onset, mid-tropospheric lows and tropical cyclone behaviour. Mon. Wea. Rev., 123,
1636-1659.
Kurihara, K., N. Davidson, K. Puri, R. Bowen and M. Ueno, 1995: Simulations of
Tropical Cyclone Connie during AMEX Phase II. Aust. Met. Mag., 45, 101-111.
McBride, J.L., N. Davidson, K. Puri and G. Tyrell, 1995: The flow during TOGACOARE as diagnosed by the BMRC tropical analysis and prediction system. Mon.
Wea. Rev., 123, 717-736.
Puri, K. and N.E. Davidson, 1993: The use of satellite data as proxy data for moisture
and diabatic heating in data assimilation. Mon. Wea. Rev., 121, 2329-2341.
Davidson, N.E. and K. Puri, 1992: Limited area tropical prediction for AMEX. Aust.
Met. Mag., 40, 179-190.
Davidson, N.E., J. Wadsley, K. Puri, K. Kurihara, and M. Ueno, 1992: Implementation
of the JMA typhoon bogus in the BMRC tropical prediction system. J. Met. Soc.
Japan, 71, 437-467.
Puri, K., N.E. Davidson, L.M. Leslie, and L.W. Logan, 1992: The BMRC tropical
limited area model. Aust. Met. Mag.,40, 81-104.
Davidson, N.E., G.J. Holland, J.L. McBride, and T.D. Keenan, 1990: On the formation
of AMEX tropical cyclones Irma and Jason. Mon. Wea. Rev., 118, 1981-2000.
Davidson, N.E. and A. Kumar, 1990: Numerical simulation of the development of
AMEX tropical cyclone IRMA. Mon. Wea. Rev., 118, 2001-2019.
Krishnamurti, T.N., A. Kumar, K-S. Yap, A.P. Dastoor, N. Davidson and J. Sheng,
1990: Performance of a high resolution meso-scale tropical prediction model.
Advances in Geophysics, 32, 133-286.
Gunn, B.W., J.L. McBride, G.J. Holland, T.D. Keenan, N.E. Davidson, and H.H.
Hendon, 1989: The Australian summer monsoon circulation during AMEX Phase II.
Mon. Wea. Rev., 117, 2554-2574.
27
Hendon, H.H., N.E. Davidson, and B.W. Gunn, 1989: Australian summer monsoon
onset during AMEX 1987. Mon. Wea. Rev., 117, 370-390.
Keenan, T.D., J.L. McBride, G.J. Holland, N.E. Davidson, and B.W. Gunn, 1989:
Diurnal variations during the Australian monsoon experiment (AMEX) Phase II.
Mon. Wea. Rev., 117, 2535-2552.
McBride, J.L., B.W. Gunn, G.J. Holland, T.D. Keenan, N.E. Davidson, and W.M.
Frank, 1989: Time series of total heating and moistening over the Gulf of
Carpentaria radiosonde array during AMEX. Mon. Wea. Rev., 117, 2701-2713.
Mills, G.A. and N.E. Davidson, 1987: Tropospheric moisture profiles from digital IR
satellite imagery: System description and analysis/forecast impact. Aust. Met. Mag.,
35, 109-118.
Keenan, D.W., N.E. Davidson, and G.A. Kelly, 1986: Analysis of screen level
temperature and dewpoint in the Australian region. Aust. Met. Mag, 34, 155-161.
Davidson, N.E., J.L. McBride, B.J. McAvaney, 1984: Divergent circulations during the
onset of the 1978-79 Australian monsoon. Mon. Wea. Rev., 11, 1684-1696.
Davidson, N.E., 1984: Short term fluctuations in the Australian monsoon during winter
MONEX. Mon. Wea. Rev., 112, 1697-1708.
Davidson, N.E., J.L. McBride, and B.J. McAvaney, 1983: The onset of the Australian
monsoon during winter MONEX: Synoptic aspects. Mon. Wea. Rev., 111, 496-516.
Davidson, N.E., 1982: Diagnostic capabilities of objective analysis schemes: areal
mean vertical motion fields of some weather regimes over Australia. Aust. Met.
Mag., 30, 211-221.
Davidson, N.E. and B.J. McAvaney, 1981: The ANMRC tropical analysis scheme.
Aust. Met. Mag., 29, 155-168.
Participation in Conference/Seminar/Workshop/ Summer Schools
Invited Presentations:
WMO World Weather Research Programme (WWRP) and Tropical Cyclone
Programme (TCP), International Workshop on Rapid Change Phenomena in Tropical
Cyclones. Haikou, Hainan, China. 5-9 November 2012.
Advanced Indo-US Training Workshop and Colloquium on Modeling and Data
Assimilation for Tropical Cyclone Predictions, 9-14 July, 2012, Indian Institute of
Technology, Bhubaneswar, Odisha, India.
IUGG, Symposium on Tropical Cyclones, Montreal, Canada, 19-29 July 2009.
International Meeting on South Atlantic Cyclones, Track Prediction and Risk
Evaluation, Cooperative Institutions of Brazil, Rio de Janeiro, May 19 – 21, 2008.
Regional Research Workshop on Tropical Cyclones. Meteo France. RSMC La Reunion.
May 26 -30, 2008.
28
Planning Workshop for the THORPEX Pacific Asian Regional Campaign (T-PARC),
Tropical Cyclone Structure (TCS08) and other Collaborative Experiments.
Princeville, Kauai, Hawaii, 4-6 December 2007.
Recent Advances in Understanding Tropical Cyclones, Monash University, 23 March
2007.
IUGG, Symposium on Intense Vortices, Sapporo, Japan, 30 June – July 11, 2003
International Workshop on Tropical Cyclone Intensity Change, SanDiego, USA, 3 – 5
May,2002.
WMO Workshop on Typhoon Forecasting Research. Cheju, Korea, 25 - 28 September ,
2001.
International Workshop on the Dynamics and Forecasting of Tropical Weather Systems.
Darwin, Australia, 22 - 26 January, 2001.
Workshop on Spatial Objective Analysis for Diagnostic Studies in Meteorology and
Oceanography. Menorca, Spain, 18 - 22 September 2000
Recent collaborations outside of CAWCR:
Prof. Michael Montgomery, Naval Postgraduate School, Monterey
Prof. Roger Smith, University of Munich
Dr. Craig Bishop, Naval Research Laboratory, Monterey
Dr. Harry Weber, University of Munich
Dr. Mathieu Plu, Meteo-France
Prof. Zhexian Luo, Chinese Academy of Science
Prof. Ping Fan, Chinese Academy of Science
Prof. Michael Reeder, Monash University.
Dr. Alex Pezza, University of Melbourne
Research Guidance:
No. of Ph.D. students enrolled/ completed: 5
Chi Mai Nguyen, Monash University. Inner-core vacillation cycles during the rapid
ntensification of Hurricane Katrina. PhD dissertation, Department of Mathematical
Sciences, Monash University, (accepted January 2011) (with Prof. M.J. Reeder)
Luke A. Garde, University of Melbourne. Tropical and Extratropical Transition of
Tropical Cyclones in the Australian Region. PhD Candidate, School of Earth Sciences,
University of Melbourne. (with Dr. A. Pezza)
Marie-Dominique Leroux, Meteo-France. Based on research she completed while an
Intern at CAWCR, she successfully applied to continue this work as part of her PhD.
Funding for her PhD comes from a Meteo-France Scholarship. (with Mathieu Plu,
Frank Roux and David Barbary, Meteo-Franc)
29
Lili Liu, Institute of Atmospheric Physics, Beijing, China. Downstream Development
during the Extratropical Transition of Tropical Cyclones: Observational Evidence and
Influence on Storm Structure. Her PhD was based on research she commenced and
mostly completed while an Intern at CAWCR, working on a project defined and
supervised by Noel Davidson and Hongyan Zhu. (accepted September 2010).
Ying Jun Chen, University of Melbourne. PhD student. Rainfall in Tropical Cyclones.
(With Drs. Kevin Walsh and Beth Ebert).
No. of Graduate/Postgraduate students enrolled/ completed: 2
Charlie Lok, Monash University. Landfall of Typhoons near Hong Kong. MSc Student.
(accepted June 2012) (with Prof. M.J. Reeder)
Joan Fernon, Monash University. The Australian Monsoon. MSc student. (With Prof.
M.J. Reeder)
Synergistic Activities:
Activity 1
Recent Developments of Advanced Operational NWP Systems
TC-LAPS (2004-2010): This system is built around a unique initialization technique
which (a) builds the inner-core structure (central pressure, maximum wind at the radius
of maximum wind) based on observed storm structure, (b) re-locates the circulation to
the observed location, (c) imposes a steering flow consistent with the current motion,
and (d) preserves the analysed primary circulation, while initializing the secondary
circulation using model dynamics, physical process and satellite imagery to redefine the
vertical motion field and asymmetries in convection. Balanced initial conditions are
crucial for accurate prediction of TCs and this technique enabled TC-LAPS to achieve
track and intensity forecasting skill competitive with the very best available.
ACCESS-TC (2011 - ): Like TC-LAPS, the initialization (a) builds the inner-core
structure (central pressure, maximum wind at the radius of maximum wind) based on
observed storm structure, (b) re-locates the circulation to the observed location, and (c)
imposes a steering flow consistent with the current motion. Then based on a minimal
distribution of only synthetic surface pressure observations, the power of the 4DVAR is
used to build the horizontal and vertical structure of the inner core, and initializes the
secondary circulation. The system was declared operational in November 2011.
Activity 2
Leading three major projects: (i) ACCESS-TC for operations and research, (ii) the
NOPP/ONR Project on rapid intensification of TCs, and (iii) an ACCSP Project on
Rossby Wave Activity and Extreme Weather. These mostly define my current interests.


ACCESS-TC for operations and research
Initialization, prediction and understanding of the rapid intensification of
30






tropical cyclones
Impact of initial vortex structure on track, intensity and structure change
Mechanisms of extratropical transition of tropical cyclones
Influence of amplifying, propagating Rossby waves on tropical cyclone structure
change
Extratropical-tropical interaction during the Australian summer monsoon
Rossby wave activity and rain processes during extreme weather events in the
Australian Region
Prediction and diagnosis of TC genesis.
31
8.4 Co-PI (2) Biography (Dr. Kamal Puri)
Name: Kamal Puri
Date of birth:
Institution:
Address (Residence): 89 Glenburnie Road, Vermont, Victoria 3133, Australia
Tel. No. : +61-3-9874-5168
Mob No: +61 438324953
E-mail Id: K.Puri@bom.gov.au
Address (Office): 700 Collins Street, Docklands, Victoria 3008, AUSTRALIA
Tel. No. :- +61-3-9669-4433
FAX:- +61-3-9669-4660
Official E-mail Id:- K.Puri@bom.gov.au
Official website address:- http://cawcr.gov.au/
Educational Qualification:
School/College/University
University of Manchester,
UK
University of Manchester,
UK
University of Manchester,
UK
Degree
B.Sc. Hons
Year Main subjects
1968 Physics
Diploma of
1969 Physics
Advanced Studies
in Science
PhD
1972 Physics
Division/Class
2.1
Distinction
Awards / Honors / Fellowship etc.:
1992-1994
1992-1996
1987-1990
1988-Present
1990-2000
1991-1995
1995 - 2000
1995 -2009
1998 - 2004
20012001 2005 -
Associate Editor, Monthly Weather Review
Associate Editor, Quarterly Journal of the Royal Meteorological
Society (QJRMS)
Member, WMO/CAS Working Group on Tropical Meteorology
Member, WMO/CAS Working group on Limited Area
Modelling/Tropics
Chairman, WMO/CAS Long term Asian/African Monsoon Studies
(Project M2)
Member, WMO/CAS Monsoon Experimentation Group (MONEG)
Member WMO/CAS Research Initiative on East Asian Monsoon
(Project M1)
Member WMO/CAS Working Group on Numerical Experimentation
(WGNE)
Chairman, WMO/CAS Working Group on Numerical Experimentation
(WGNE)
Member, Scientific Advisory Group of the GEWEX Coordinated
Enhanced Observing Period (CEOP)
Member, THORPEX International Core Steering Committee (ICSC)
Member, THORPEX Science Advisory Board
32
2005 2005 2005 2008 2011 -
Member, THORPEX Executive Board
Co-Chair, Southern Hemisphere THORPEX Regional Committee
Member, UK Met Office Science Advisory Committee
Member, Government of India Ministry of Earth Sciences International
Advisory Panel (IAP)
Member, Science Advisory Committee, Korea Institute of Atmospheric
Prediction Systems (KIAPS).
Appointments (Professional experience/employment record):
Organization
CSIRO
BMRC
CAWCR
Designation / Position
RS, SRS, PRS
PRS, SPRS
SPRS
Duration ( Year / date)
1974 - 1984
1985 - 2005
2005 -
List of publications:
Puri, K., and W.P. Bourke, 1974: Implications of horizontal resolution in spectral
model integrations. Mon. Wea. Rev., 102, 33-47.
Bourke, W.P., B.J. McAveney, K. Puri, and R.J. Thurling, 1977: Global modelling of
atmospheric flow by spectral methods. Methods in Computational Physics, Vol 17,
General Circulation models of the Atmosphere, Academic Press, 267-324.
McAveney, B.J., W.P. Bourke, and K. Puri, 1978: A global spectral model for
simulation of the general circulation. J. Atmos. Sci., 35, 1557-1583.
Wells, N.C., and K. Puri, 1979: Atmospheric feedback in a coupled ocean-atmosphere
model. J. Geophys. Res., 84, 4985-4997.
Daley, R., and K. Puri, 1980: Four-dimensional data assimilation and the slow
manifold. Mon. Wea. Rev., 108, 85-99.
Puri. K., 1981: Extended range forecasts for the Southern Hemisphere with the
ANMRC spectral model. Mon. Wea. Rev., 109, 286-305.
Kasahara, A., and K. Puri, 1981: Spectral representation of three-dimensional global
data by expansion in normal model functions. Mon. Wea. Rev., 109, 37-51.
Bourke, W.P., K. Puri, and R. Seaman, 1982: Numerical weather prediction studies
from FGGE Southern Hemisphere data base. Mon. Wea. Rev., 110, 1787-1800.
Puri K., W.P. Bourke, and R. Seaman, 1982: Incremental linear normal mode
initialisation in four dimensional assimilation. Mon. Wea. Rev., 110, 1773-1784.
Bourke, W.P., K. Puri, R. Seaman, B. McAveney, and J. Le Marshall, 1982: ANMRC
data assimilation for the Southern Hemisphere. Mon. Wea. Rev., 110, 1749-1771.
Puri, K., and W.P. Bourke, 1982: A scheme to retain the Hadley circulation during
non-linear normal model initialisation. Mon. Wea. Rev., 110, 327-335.
Puri, K., 1983: Some experiments in variational normal mode initialisation in data
assimilation. Mon. Wea. Rev., 111, 1208-1218.
33
Puri, K., 1983: The relationship between convective adjustment, Hadley circulation and
normal modes of the ANMRC spectral model. Mon. Wea. Rev., 111, 23-33.
Pitcher, E.J., R.C. Malone, V. Ramanathan, M.L. Blackmon, K. Puri, and W.P. Bourke,
1983: January and July simulations with a spectral general circulation model. J.
Atmos. Sci., 40, 580-604.
Malone, R.C., E.J. Pitcher, M.L. Blackmon, K. Puri, and W.P. Bourke, 1984: The
simulation of stationary and transient geopotential-height eddies in January and July
with a spectral general circulation model. J. Atmos. Sci., 41, 1394-1419.Puri, K.,
1984: The ANMRC data assimilation system - the impact of normal mode
initialisation techniques. The 1984 ECMWF Seminar, Data Assimilation Systems and
Observing System Experiments with particular emphasis on FGGE, Vol 1, 235-279.
Puri, K., and W. Stern, 1984: Investigations to reduce noise and improve data
acceptance in the FGGE 4-dimensional analysis system.. The 1984 ECMWF
Seminar, Data Assimilation Systems and Observing System Experiments with
particular emphasis on FGGE, Vol 2, 157-190.
Frederiksen, J.S., and K. Puri, 1985: Nonlinear instability and error growth in Northern
Hemisphere three-dimensional flows: Cyclogenesis, onset of blocking and mature
anamolies. J. Atmos. Sci., 42, 1374-1397.
Bourke, W.P., R. Seaman, and K. Puri, 1985: Data assimilation. Advances in Geophys.,
28B, Atmospheric and Oceanic Modelling, Academic Press, 123-155.
Puri, K., 1985: Sensitivity of low-latitude velocity potential field in a numerical
weather prediction model to initial conditions, initialisation and physical processes.
Mon. Wea. Rev., 113, 449-466.
Puri, K., and D.J. Gauntlett, 1987: Numerical weather prediction in the tropics. J. Met.
Soc. Japan, Special Volume, Ed. T. Matsuno, 605-631.
Puri, K., 1987: Some experiments on the use of tropical diabatic heating information for
initial state specification. Mon. Wea. Rev., 115, 1394-1406.
Heckley, W.A., and K. Puri, 1988: The Winter Monsoon during AMEX. A quick look
atlas 10 January - 15 February 1987. ECMWF Publication, 169pp.
Puri, K., 1988: On the importance of low frequency gravity modes for the evolution of
large scale flow in a general circulation model. J. Atmos. Sci., 45, 2523-2544.
Puri, K., P. Lonnberg, and M.J. Miller, 1990: The ECMWF analysis-forecast system
during AMEX. ECMWF Tech. Report No. 65, 166pp.
Puri, K., and M.J. Miller, 1990: Use of satellite data in the specification of convective
heating for diabatic initialisation and moisture adjustment in numerical prediction
models. Mon. Wea. Rev., 118, 67-93.
Puri, K., and M.J. Miller, 1990: Sensitivity of ECMWF analyses-forecasts of tropical
cyclones to cumulus parameterization. Mon. Wea. Rev., 118, 1709-1741.
Puri, K., 1990: Tropical numerical weather prediction studies for the 1987 Australian
summer monsoon. Mausam, 41, 257-264.
34
Puri, K., and P. Lonnberg, 1991: Use of high resolution structure functions and
modified quality control in the analysis of tropical cyclones. Mon. Wea. Rev., 119,
1151-1167.
Puri, K., N.E. Davidson, L.M. Leslie, and L.W. Logan, 1992: The BMRC tropical
limited area model. Aust. Met. Mag., 40, 81-104.
Puri, K., and N.E. Davidson, 1992: The use of satellite data as proxy data for moisture
and diabatic heating in data assimilation. Mon. Wea. Rev., 120, 2329-2341.
Davidson, N.E., and K. Puri, 1992: Tropical prediction using dynamical nudging,
satellite-defined convective heat sources, and a cyclone bogus. Mon. Wea. Rev., 120,
2501-2522.
Davidson, N.E., J. Wadsley, K. Puri, K. Kurihara, and M. Ueno, 1992: Implementation
of the JMA typhoon bogus in the BMRC tropical prediction system. J. Met. Soc.
Japan, 71, 437-467.
Puri, K., N.E. Davidson, and P. Steinle, 1995: Assimilation of information on moisture
and diabatic heating from satellite imagery. The 1993 ECMWF Seminar,
Developments in the use of satellite data in numerical weather prediction, 349-398.
Puri, K., 1995: Modelling studies on the Australian summer monsoon. Mon. Wea. Rev.,
122, 2816-1837.
McBride, J.L., N.E. Davidson, K. Puri, and G.C. Tyrell, 1995: The flow during TOGA
COARE as diagnosed by the BMRC Tropical Analysis and Prediction System. Mon.
Wea. Rev., 123, 717-736.
Kurihara, K., N.E. Davidson, K. Puri, R. Bowen, and M. Ueno, 1996: Simulations of
tropical cyclone Connie from the Australian Monsoon Experiment. Aust. Met. Mag.,
45, 101-111.
Puri, K., and G.A. Mills, 1996: Initial state specification for mesoscale applications. J.
Met. Soc. Japan, 75 No.1B, 286-413.
Davidson, N.E., K. Kurihara, T. Kato, G. Mills, and K. Puri, 1998: Dynamics and
prediction of a mesoscale, extreme rain event in the Baiu front over Kyushu, Japan.
Mon. Wea. Rev., 126, 1608-1629.
Puri, K., G.S. Dietachmayer, G.A. Mills, N.E. Davidson, R.A. Bowen, and L.W.
Logan, 1998: The new BMRC Limited Area Prediction System, LAPS. Aust. Met.
Mag. , 47, 203-223.
Saito, K., T. Keenan, G.J. Holland, and K. Puri, 2000: Diurnal evolution of tropical
island convection over the maritime continent. Mon. Wea. Rev., 121, 378-400.
Puri, K., J. Barkmeijer, and T.N. Palmer, 2001: Ensemble prediction of tropical
cyclones using diabatic singular vectors. Q .J. R. Met. Soc., 127, 709,731.
Barkmeijer, J., R. Buizza, T.N. Palmer, K. Puri and J.-F. Mahfouf, 2001: Tropical
singular vectors computed with linearized diabatic physics. Q. J. R. Met. Soc., 127,
685-708.
Sperber, K.R., C. Brankovic, M. Deque, C.S. Frederiksen, R. Graham, A. Kitoh, C.
Kobayashi, T. Palmer, K. Puri, W. Tennant and E. Volodin, 2001: Dynamical
35
seasonal predictability of the Asian summer monsoon. Mon. Wea. Rev., 129, 22262248.
Cope, M.E., G.D. Hess, S. Lee, K. Tory, M. Azzi, J. Carras, W. Lilley, P.C. Manins, P.
Nelson, L. Ng, K. Puri, N. Wong, S. Walsh and M. Young, 2003: The Australian
Air Quality Forecasting System. Part I. Project description and early outcomes. J.
App. Met., 43, 649-662.
Hess, G.D., K.J. Tory, M.E. Cope, S. Lee, K. Puri, P.C. Manins and M. Young, 2003:
The Australian Air Quality Forecasting System. Part II. Case study: Sydney 7-day
photochemical-smog event. J. App. Met., 43, 663-679.
Tory, K.J., M.E. Cope, G.D. Hess, S. Lee, K. Puri, P.C. Manins and N. Wong, 2003:
The Australian Air Quality Forecasting System. Part III. Case study: Melbourne 4day photochemical-smog event. J. App. Met., 43, 680-695.
Puri, K., 2005. Blueprint for ACCESS. Unpublished Bureau of Meteorology report.
Puri, K., 2005. Project plan for ACCESS. Unpublished Bureau of Meteorology report.
Puri, K., 2012. The Australian Community Climate and Earth System Simulator:
Scientific justification and options for System development. Accepted for publication
to Aust. Met. Oceanog. J.
Puri, K., Dietachmayer, G., Steinle, P., Dix, M., Rikus, L., Logan, L., Naughton, M.,
Tingwell, C., Xiao, Y., Barras, V., Bermous, I., Bowen, R., Deschamps, L., Franklin,
C., Fraser, J., Glowacki, T., Harris, B., Lee, J., Le, T., Roff, G., Sulaiman, A., Sims,
H., Sun, X., Sun, Z., Zhu, H., Chattopadhyay, M., and Engel C. 2012,
Implementation of the initial ACCESS Numerical Weather Prediction system.
Submitted for publication to Aust. Met. Oceanog. J.
Dix, M., Vohralik, P., Bi, D., Rashid, H., Marsland, S., O’Farrell, S, Uotila, P., Hirst,
A., Kowalczyk, E., Sullivan, A, Yan, H., Franklin, C., Sun, Z., Watterson, I., Collier,
M., Noonan, J., Rotstayn, L., Stevens, L., Uhe, P. and Puri, K., 2012. The ACCESS
Coupled Model: Documentation of core CMIP5 simulations and initial results.
Submitted for publication to Aust. Met. Oceanog. J.
Bi, D., Dix, M., Marsland, S., O’Farrell, S., Rashid, H., Uotila, P., Hirst, A.,
Kowalczyk, E., Golebiewski, M., Sullivan, A, Yan, H., Hannah, N., Franklin, C.,
Sun, Z., Vohralik, P., Watterson, I., Zhou, X., Fiedler, R., Collier, M., Ma, Z.,
Noonan, J., Stevens, L., Uhe, P., Zhu, H., Griffies, S., Hill, R., Harris, C., and Puri,
K., 2012. The ACCESS Coupled Model: Description, control climate and evaluation.
Submitted for publication to Aust. Met. Oceanog. J.
Sun, Z., Franklin, C., Zhou, X, Ma, Y., Okely, P., Bi, D., Dix, M., Hirst, A., Shonk,
J.K.P, and Puri, K., 2012. Modifications in atmospheric physical parameterization
aimed at improving SST simulation in the ACCESS coupled-model. Submitted for
publication to Aust. Met. Oceanog. J.
36
9. List of supplementary documents :
( e.g., authorization letter from the Head of the organization, endorsements etc.)
Letter of support from Dr. Peter May, Deputy Director of CAWCR
10. References cited:
Ashrit, R.G., E.N. Rajagopal, G.R. Iyengar, A.K. Mitra and A.K. Bohra, 2009: Rainfall
forecast verification during Monsoon 2008: T254 Vs UKMO. NCMRWF Research
Report NMRF/RR/2/2009.
Ashrit, R.G., 2012: Inter-comparison of Rainfall forecasts: Global (UKMO) and
Regional (SAMU) Models. Report in preparation.
Bohra, A.K., Swati Basu, E. N. Rajagopal, G. R. Iyengar, M. Das Gupta, R. Ashrit and
B. Athiyaman, 2006: Heavy rainfall episode over Mumbai on 26 July 2005:
Assessment of NWP guidance. Current Science, 90, 1188 -1194.
Bowler, N.E., A. Arribas, K.R. Mylne, K.B. Robertson, S.E. Beare, 2008: The
MOGREPS short-range ensemble prediction system. Quart. J. Royal Meteorol. Soc.,
134, 703–722.
Davidson, N.E., K. Kurihara, T. Kato, G. Mills and K. Puri, 1998: Dynamics and
prediction of a mesoscale, extreme rain event in the Baiu front over Kyushu, Japan.
Mon. Wea. Rev. , 126, 1608-1629.
Ebert, E.E., 2008: Fuzzy verification of high resolution gridded forecasts: A review and
proposed framework. Meteorol. Appl., 15, 51-64.
Ebert, E.E. and J.L. McBride, 2000: Verification of precipitation in weather systems:
Determination of systematic errors. J. Hydrology, 239, 179-202.
Iyengar, Gopal R., R.G. Ashrit, M. Das Gupta, Manjusha Chourasia, Kuldeep Sharma,
V.S. Prasad, E.N. Rajagopal, A.K. Mitra, Saji Mohandas and L. Harenduprakash,
2010: NCMRWF & UKMO Global Model Forecast Verification: Monsoon 2010
Mass, C.F., D. Ovens, K. Westrick and B.A. Colle, 2002: Does increasing horizontal
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