PROPOSAL
Consultancy to establish an integrated early warning system for REMA
Technical Proposal: approach, methodology and work plan
Proposal being submitted to:
RWANDA ENVIRONMENT MANAGEMENT AUTHORITY: TENDER REF: No 022/REMA/20112012
Principal Investigator
Thomas M. Hopson, Ph.D.
National Center for Atmospheric Research
P.O. Box 3000
Boulder, CO 80307
Ph: +1 303-497-2706
Fax: +1 303-497-8401
Email: hopson@ucar.edu
Co-Principal Investigators
Brant Foote, Arnaud Dumont, and David Gochis
National Center for Atmospheric Research
P.O. Box 3000
Boulder, CO 80307
Start Date: 01 October 2012
End Date: 30 March 2014
Duration: 1.5 years
Total Budget for Duration: US$
Due Date: 23 April 2012
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Proposal Summary
Rwanda has experienced flash floods impacting urban areas that are difficult to measure
and anticipate. As a result, the country is in the process of improving both its measurement
capability as well as its flash flood forecasting capabilities through installation of advanced weather
and forecast hardware and software, and through training operational personnel. In addition, the
country’s meteorological office is establishing closer collaborations with disaster management
institutions and collaborations with local communities to configure early warning systems and
strengthen dissemination capacity. To help meet these needs, the Research Applications
Laboratory (RAL) of the National Center for Atmospheric Research (NCAR) offers its science and
technology capability to enhance Rwanda’s capacity to meet its forecasting requirements.
Given the above context of Rwanda’s vulnerability to severe flooding events and current
lack of established infrastructure, NCAR/RAL proposes a phased response to enable Rwanda to
establish a foundation to build longer-term infrastructure and technical staff capacity. This Phase-1
proposal entails (1) an assessment of current infrastructure, capacity, user needs, and training; (2)
acquiring, assessing, and utilizing publicly-available data resources; (3) implementing a data and
early warning dissemination and visualization infrastructure that will integrate publicly available
products and will have the flexibility to incorporate advanced future data sets (e.g., radar, rain
gauges, streamflow observations, advanced NWP forecasting); and (4) consulting with and training
key Rwanda personnel to anticipate and utilize future data resources that would have relevancy to
precipitation and flooding Early Warning Systems (EWS). In this context, we have broken down the
tasking of this proposal into four areas: early assessment, development of publicly-available data
utilization for EWS, decision support (DSS), and capacity enhancement through
consultation/training. We discuss each of these pieces individually below, providing a time-line and
tasking of deliverables at the end of this document.
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1. Introduction _________________________________________________________________________________________ 4
1.1 A roadmap to developing a state-of-the-art Early Warning System ________________________ 4
1.2 Scope of the current proposal ____________________________________________________________________ 4
2. “Phase 1” Activities _________________________________________________________________________________ 6
2.1 Assessment of Capabilities and Needs __________________________________________________________ 6
Task 1: Prepare for Site Visits by Collecting and Reviewing Background Information ______________________ 7
Task 2: Conduct Site Visit ________________________________________________________________________________________ 8
Task 3: Analyze Results of Site Visit and Prepare Report ______________________________________________________ 8
2.2 Utilization of publicly available data sources __________________________________________________ 9
2.2.1 Satellite-based precipitation estimation _________________________________________________________________10
2.2.2 Thorpex-Tigge Medium-range Weather prediction _____________________________________________________10
2.3 Information Management and Decision Support Systems (DSS) for Weather and Flood
Forecasting ______________________________________________________________________________________________10
2.3.1 Information Management _________________________________________________________________________________11
2.3.2 Standardized Formats and Interfaces ____________________________________________________________________11
2.3.3 Data Repository and Replication _________________________________________________________________________12
2.3.4 Decision Support __________________________________________________________________________________________12
2.3.5 Data Services _______________________________________________________________________________________________13
2.4 Training _____________________________________________________________________________________________13
3. Work Plan ___________________________________________________________________________________________ 14
3.1 Assessment _________________________________________________________________________________________14
3.1.1 Tasks _______________________________________________________________________________________________________14
3.1.2 Budget ______________________________________________________________________________________________________14
3.1.3 Timeline ____________________________________________________________________________________________________14
3.2 Utilization of publicly-available large-scale datasets ________________________________________15
3.2.1 Tasks _______________________________________________________________________________________________________15
3.2.2 Budget ______________________________________________________________________________________________________15
3.2.3 Timeline ____________________________________________________________________________________________________15
3.3 Information Management and Decision Support Systems (DSS) for Weather and Flood
Forecasting ______________________________________________________________________________________________15
3.3.1 Tasks _______________________________________________________________________________________________________15
3.3.2 Budget ______________________________________________________________________________________________________16
3.3.3 Timeline ____________________________________________________________________________________________________16
3.4 Training _____________________________________________________________________________________________16
3.4.1 Tasks _______________________________________________________________________________________________________16
3.4.2 Budget ______________________________________________________________________________________________________16
3.4.3 Timeline ____________________________________________________________________________________________________16
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1. Introduction
1.1 A roadmap to developing a state-of-the-art Early Warning System
Acquiring a truly effective, state-of-the-art environmental Early Warning (EWS) System and
decision support tool requires development across many different levels (or types) of
infrastructure. Such a system requires in-situ (e.g., rain gauge networks) and remotely-sensed (e.g.,
radar) data sources, algorithmic developments, data transfer and visualization to meet specific user
needs, training and capacity building, and awareness. In Figure 1, we diagrammatically highlight
these required levels of development.
Fig. 1: Early Warning System linkages for weather-related hazards
1.2 Scope of the current proposal
Developing the EWS described in Figure 1 is an extremely significant undertaking which has three
underlying requirements:

Requirement 1: Good understanding of the physical environment, observational and
modelling infrastructure, and human resources as they currently exist, as well as
understanding what is needed by decision makers at the local and national levels.
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Requirement 2: Local-scale information from rain gauge networks and satellites coupled
with meteorological and hydrological numerical prediction systems tuned to the local
region (Levels 1 and 2).
Requirement 3: An integration and product generation and dissemination system designed
within the framework of Requirements 1 and 2 that addresses the user needs and societal
vulnerabilities (Level 3).
We see clear recognition in the terms of reference of the need for an assessment of needs and
capabilities within Rwanda (addressing requirement #1 above); and we note that REMA is
developing networks of rain gauges and installing radars under separate tenders (addressing part
of requirement #2 above). However, while solid plans are in place, the above three
requirements do not currently exist. In our opinion it is not possible to create a state-of-theart early warning system (e.g., provide accurate quantitative flood forecasts) without local
scale information.
In terms of scope, we propose a multi-phased program leading to establishment of an EWS as
described in the terms of reference. In this proposal we describe work that will cover Phase 1
of the program. This phase will address many of items identified in the terms of reference,
including:
1. Assessment activities as identified in the terms of reference, and including: assessing
current forecasting capabilities for the region; reviewing available model and observation
data sets and their performance over the region; identifying and understanding user needs;
identifying ways to accelerate improvements in the prediction of high-impact events,
understanding what is needed to improve current forecasts using satellite data and
ensemble model forecasts; investigating modeling needs; examining current
communications infrastructure; and evaluating needs for in-country training.
2. The development of a preliminary and basic decision support tool that will initially ingest
currently-available data, including that from satellites and global-scale numerical weather
prediction models, and any available ground-based observational systems. This tool will be
custom-built for REMA and will serve as the engine for the fully-fledged EWS developed in
future phases of the project.
A future Phase 2 (or perhaps multiple phases) of the project – not described in this proposal
– is needed to focus on integrating new sources of data as they become available (e.g., data
from rain-gauge networks and radar that are currently under development, and from advanced
numerical prediction systems) so as to provide new advanced forecast capabilities to the nation.
Infrastructure acquisitions currently being pursued by the Government of Rwanda will drive this
progress. The addition of a radar, for example, will allow us to bring NCAR’s world-class nowcasting
expertise to the EWS. The availability of cluster computing will allow us to bring our extensive skill
in atmospheric and hydrological numerical modeling to the EWS. Improved ability to assimilate
observational and modeling data will allow us to create a cutting-edge decision support system,
another capability for which NCAR is well-known.
It is acknowledged that we are not proposing to provide all of the deliverables described in the
terms of reference. In good faith, we do not believe that is possible to promise these things before
conducting a detailed assessment of existing infrastructure, detailed user needs, and human
capacity, and before rain gauge networks are established and a meteorological radar is operational.
We have extensive experience building end-to-end decision support systems and can attest that the
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required research, development, technology transfer and training effort requires multi-year
institutional commitments and flexibility to evolving (and often unanticipated) data and
computational resources to be responsibly carried out. However, the work proposed in a Phase 1
here by NCAR will put REMA firmly on the path to achieving its goals by conducting a thorough
assessment, providing some immediate improvements in forecasting skills, and planning and laying
the framework for the development and deployment of an advanced operational EWS.
2. “Phase 1” Activities
2.1 Assessment of Capabilities and Needs
An assessment of current forecast capabilities and the needs of the nation of Rwanda is the first,
and most critical, task to be performed by NCAR. To do this, we propose to engage the REMA
sponsor in a dialogue concerning the “art of the possible.” By this we mean an interactive process in
which we exchange information to identify needs and challenges on the user’s side and the kinds of
improvements science and technology can currently provide. The outcome from this task will be
a realistic, honest appraisal of current capabilities and needs, prioritization of those needs,
and identification of the requirements for a state-of-the-art early warning system.
This approach to assessing a new sponsor’s needs and baseline capabilities has been successfully
used by NCAR in research and development projects for U.S. mission agencies such as the Federal
Aviation Administration, NOAA, NASA, and Department of Defense, as well as for government
agencies in Hong Kong, Taiwan, Europe, Latin America, the Middle East, and Australia.
NCAR has assembled a scientific and technical team with extensive, relevant experience in a variety
of fields essential to the success of the assessment. This team will work closely with REMA to assess
the current state of forecasting capabilities and needs in Rwanda. The evaluation effort will be a
collaborative effort, combining the experience and expertise of NCAR team with the expert, incountry knowledge of REMA staff.
Core NCAR Team:
 Dr. Tom Hopson: Program Manager, hydrologist with ensemble modeling expertise.
 Dr. Brant Foote: Laboratory Director and senior atmospheric scientist with extensive
international experience in assessment of forecasting systems and field programs.
 Dr. David Gochis: Senior hydrometeorologist, with experience in design of rain gauge
networks, and experience in development of coupled atmosphere-hydrology prediction
models.
 Mr. Arnaud Dumont: Senior software engineer, lead developer of numerous complex
decision support systems.
 Mr. Tor Mohling: Senior systems administrator/information technology expert.
Additional Expert Resources:
One of the strengths of the NCAR proposal is the large reservoir of talented staff members
available to the REMA effort. As specific questions or issues arise, experts on the NCAR staff will be
consulted to make use of their in-depth skills and insights. Some examples include:
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


Mr. Jim Wilson and Ms. Rita Roberts: Senior researchers and international leaders in radar
meteorology and short-term forecasting (nowcasting).
Ms. Cathy Kessinger: Senior researcher and expert in satellite meteorology.
Mr. John Exby: Senior systems administrator and expert in cluster computing.
Dr. Paul Kucera: Senior researcher and expert in precipitation instrumentation.
The assessment of forecasting capabilities will be conducted in three phases:
1. NCAR will provide a questionnaire to REMA to obtain as much information as possible upfront; the NCAR team will closely review this information to gain an understanding of the
nation’s current capabilities and needs before traveling to Rwanda;
2. NCAR will send a team of scientific and technical experts to Rwanda to meet with officials in
Kigali and staff in study regions, learn about the nation’s rivers, watersheds and
topography, and get a hands-on look at existing infrastructure;
3. After the site visit, the NCAR team will follow-up with REMA to ask questions and exchange
information, and will prepare a report documenting results of the assessment task.
To enhance the collaborative exchange of information and to begin the important training
component of the program, NCAR team members would be pleased to present a number of short
introductory lectures of relevance to REMA (with further materials on these topics provided during
the training at the end of the consultancy period of performance). Specific topics could include:
 Severe Rain and Flooding Early Warning Systems of the U.S.
 Thunderstorm Nowcasting
 Radar Meteorology
 Satellite Meteorology
 The Weather Research and Forecasting (WRF) Model
 Hydrological Modeling
 Optimal Decision Support Strategy
Task 1: Prepare for Site Visits by Collecting and Reviewing Background Information
The initial step in the assessment of existing forecasting capabilities and needs will be a review of
relevant background materials supplied by REMA. These materials will be reviewed in advance of
the main site visit by the NCAR technical team. Examples of information useful to this purpose
include the following:
 REMA and Rwanda Meteorological Service staff expertise and education and training
backgrounds
 Characterization of severe flooding events (time- and spatial-scale, seasonality, vulnerable
catchments)
 Current and anticipated meteorological and hydrologic instrumentation (numbers and
locations, maintenance and calibration schedules, period-of-record)
 Weather-related hazard mitigation priorities
 Land-use, soil-types, and, anticipated population demographics, and other environmental
considerations
 Infrastructure for technological development
In collaboration with REMA staff, we will plan the site visit so that our team will be able to visit
critical sites (i.e., the Gishwati ecosystem study area), visit instrumentation sites and computing
facilities, and meet with the appropriate officials and stakeholders in our time in-country.
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Task 2: Conduct Site Visit
Capability Assessment
Following the examination of background materials the NCAR team will send a group of 4-5
scientific and technical experts to Rwanda for direct meetings with REMA and those persons at the
national and regional/local levels who will be involved in the project. It will be important for us to
understand the current infrastructure and organization for providing forecasting services and to
get to know the people with whom we will be working. In Kigali, we would expect to visit any
centralized forecasting, computing, or information technology/data processing facilities. We would
then visit the Gishwati watershed to begin to understand this unique ecosystem and its specific
hydrological, land surface, and atmospheric conditions. We would plan to visit any meteorological
instrumentation in the area, review any records that may be kept, and meet with individuals who
know the region well.
Subject to discussions and guidance from REMA, the site visits will include consideration of the
following aspects of the current hydrometeorological capabilities:
 Surface observational systems
 Upper air soundings (radiosondes)
 Satellite data reception and processing
 Communication networks (both WAN and LAN) and technologies for data transfer and
exchange between Rwandan facilities
 Use of numerical models
 Meteorologist workstations and internet access
 Age, condition, and adequacy of aeronautical meteorology instruments and equipment
 Age, condition, and adequacy of the existing support infrastructure (buildings, working
space)
 Staffing, training, maintenance, and procedures
 Potential environmental hazards or concerns
Needs Assessment
The second important component of the assessment is aimed at understanding the needs of REMA
and its key stakeholders (i.e., those who have a strong interest in the outcome of the project).
Working closely with officials and stakeholders we will discuss the following topics, as well as
others that emerge in conversation:
 What are the most critical time-scales for forecasts (e.g., hours, days, weeks, seasons)?
 What are the most pressing infrastructure needs (e.g., instrumentation, computing,
networking, data processing)?
 What would a useful early warning system look like? What should it do and how should it
work?
 How will warnings be communicated to the public? Are there improvements in public
awareness and methods of disseminating information that need to be made?
 What do forecasters in Rwanda need with regard to education and training to take
advantage of new technologies, facilities, and systems?
Task 3: Analyze Results of Site Visit and Prepare Report
The NCAR team will synthesize the data acquired from REMA, stakeholders, and the site visit, and
continue to engage with the sponsor in asking questions, exchanging information, and ensuring that
our analysis considers changes or new developments. If the Government of Rwanda pursues the
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acquisition of major new facilities such as a radar or precipitation gauges, we would clearly want to
consider how those purchases could most effectively improve current forecast capabilities.
The final step under this task is the preparation and delivery of a working paper discussing the
findings of the assessment effort. These findings will form the basis for the recommendations NCAR
provides to REMA for moving forward with a Phase 2 major implementation effort.
2.2 Utilization of publicly available data sources
Our response to this call is to first provide known technologies that are currently and publiclyavailable without reliance on incomplete in situ measurement networks and research
developments, while allowing utilization of further data resources as Rwanda’s current EWS
infrastructure continues to mature and move into specific Phase 2 developments (highlighted in the
“On the Terms of Reference” document).
Current public data sources addressed in this proposal include satellite precipitation estimates and
global NWP weather forecasts. Each of these is discussed briefly below. It must be stressed that the
skill of these estimates and forecasts depends on many factors: the temporal and spatial response
scales, the predictability of the weather and surface hydrologic systems involved, and specific user
needs. It may be unrealistic to expect that these large-scale data products will have high accuracy
at the space and time scales at which flooding occurs in the fast-response catchments in Rwanda.
As such, these large-scale publicly available data products are primarily used as context for
the subsequent development and interpretation of local-scale forecasting products
developed in future phases of the program.
Importantly, we propose to provide verification and evaluation of these current resources to
determine the spatial and temporal scales for which they may have potential to provide utility to
address regional predictability needs. Further we stress that new EWS advanced infrastructure
being pursued under different proposal calls (e.g., rain gauges and computational cluster
installation) should significantly enhance the possibilities for EWS skill, and these capabilities be
utilized in future phases of the work. The purpose of this proposal, then, is to lay a foundation for
these future applications. Data resources discussed below in this beginning Phase 1 will focus on
satellite-based precipitation estimation and NWP at medium-range spatial and temporal scales,
which have limits on their predictive capacity for Rwanda’s river basins, some of which are shown
below in Figure 2.
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Fig. 2: Map of Rwanda showing significant rivers.
2.2.1 Satellite-based precipitation estimation
Precipitation products from three satellite-based sources will be investigated for their range of
spatial and temporal scales for which they may possess remotely-sensed observational capacity
relevant to EWS system development. These sources are: the NASA-TRMM 3B42 product, the NOAA
Hydro-Estimator (H-E), and NOAA-CMORPH. H-E’s benefits are its rapid refresh capacity (hourly
rates, 15-min latency), as compared to the longer-latency NASA-TRMM and NOAA-CMORPH
products. However, by relying on the infra-red spectrum (IR) solely, the H-E is less accurate than
the TRMM/CMORPH products, which merge passive and active microwave remotely-sensed signals
with IR through distinctly-different algorithms. As a result, potential hydrometeorological
applications of these resources to update, say, regional soil moisture conditions, should be provided
by the most recently-available data set (IR-based), with a phased introduction of the merged
products (TRMM/CMORPH) as they become available.
2.2.2 Thorpex-Tigge Medium-range Weather prediction
This project would make use of the recently available THORPEX Interactive Grand Global Ensemble
(TIGGE: http://tigge.ecmwf.int/) multi-center ensemble weather data, with focus on ensemble
fields that could inform extreme precipitation predictability (see: http://tparc.mrijma.go.jp/TIGGE/tigge_extreme_prob.html). We stress that precipitation field prediction alone is
often highly limited in skill, particularly at the scales of small river catchments. For this study, we
propose to investigate regional dynamical features potentially captured within a selection of
weather fields contained in this archive, and how their diagnosis and predictability could
potentially be utilized to inform precipitation predictability.
2.3 Information Management and Decision Support Systems (DSS) for Weather and
Flood Forecasting
The variety of infrastructure and capability improvements undertaken under a weather and flood
forecasting program (Phase 2), whether for the Gishwati region, or a further country-wide coverage
extension, will result in large amounts of new data. Maximum benefit can be drawn from those data
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with the appropriate use of information management and decision support tools. Expected new
data that would be generated in support of such forecasting efforts would come from the following
sources: (1) real-time hydro-meteorological monitoring systems such as Automatic Weather
Stations (AWS), rain gauges, streamflow stations, and satellite sensors; and (2) numerical weather
prediction model forecasts (NWP) of future meteorological conditions, at different lead times
ranging from days to seasons and (3) numerical hydrological prediction of future rainfall and runoff
over the basins of interest and at different lead times. Each of these data sources will require
processing, storage, and retrieval. Decision support tools will incorporate each of these data sets in
a manner which improves their understanding and facilitates decision-making by managers and
policymakers.
2.3.1 Information Management
An effective information management system integrates processed weather forecast fields and the
other various data sources into a single information management framework. This framework is
then used to produce combined alerts, support a common visualization tool, populate a single
decision support system, and/or feed data to additional forecasting and analysis tools. The number
of data sources to integrate and the optimal depth to which they would be integrated will be
dependent on the availability of the data sources, their spatial and temporal dimensions, their
criticality to downstream modeling and visualization systems, and the final user-determined needs
of the overall system. NCAR has developed and employed a wide variety of DSS for weather and
flood forecasting, most recently in Bangladesh and Romania.
2.3.2 Standardized Formats and Interfaces
Information Management is greatly facilitated when data are available to all components via “on the
wire” standards. “On the wire” standards means that all the data sources (repositories, aggregators,
replicators) expose the data using a standard which is also supported by all of the data consumers
(DSS, aggregators, replicators, processors, and visualization). Using a common protocol for
communication and data exchange between the various components provides for maximum
flexibility in assembling data networks. Flexibility, then, allows data sources, repositories, models,
and decision support tools to be connected in novel ways for maximum benefit.
The meteorological research community has used the NetCDF format for many years to exchange
and store data. Recently, the “CF” convention of NetCDF has been adopted by an international
standards body, the Open Geospatial Consortium (OGC), as a standard format for gridded coverage
and feature-based geospatial information. Whenever possible, this format will be used for
exchanging data. When it is not feasible to use NetCDF, due to lack of support by the data producer
or consumer or due to deficiencies in the format’s ability to represent desired information, another
format may be employed. Other common formats supported by existing meteorological tools
include WMO standards, GRIB, BUFR, MDV, GML, and Geo-JSON.
The OGC standards body has developed a set of standard request and response protocols for geospatial data: Web Coverage Service (WCS), Web Feature Service (WFS), and Web Map Service
(WMS). These protocols operate over internet-standard HTTP, HTTPS, and TCP/IP protocols. They
guarantee that a data consumer will be able to formulate a request to a data provider, and that that
request will result in an understandable response. WCS is tailored to gridded coverage data, WFS is
tailored to feature (point or polygon) data, and WMS is tailored to producing presentation map
imagery. Whenever possible, one of these formats will be used for requesting and serving data.
When they are not feasible to use, due to lack of support by the data server or consumer, another
protocol supported by both consumer and producer may be substituted.
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2.3.3 Data Repository and Replication
Data repositories are an essential part of the information management system. A centralized
repository, in the form of a database or filesystem store with data services, would facilitate data
access by consumers and would be simple to manage and replicate, but may not provide the desired
level of data throughput, network accessibility, and fault-tolerance desired for a national
information management system. A distributed respository in the form of geographically disparate
data aggregators and replicators would provide more scalability, resulting in greater data
throughput, network accessibility, and fault-tolerance. A distributed data repository network would
also require significantly more effort to install and manage. Given the initial scope of this program,
it is recommended to aggregate data in a centralized repository whenever possible. This repository
will be hosted on a single computer or connected nodes and will provide coverage and feature data
access to consumers using the standard protocols described earlier. Data replication and archiving
will be performed on a parallel system, as necessary. The data repository will be initially developed
and run experimentally at NCAR, which will then be transferred to an operational system in the
field. The client will provide staff with sufficient skill in network administration and system
configuration to manage the installation and maintenance of the operational system. Future phases
of development may include extension of the initial information management system into a
distributed network.
2.3.4 Decision Support
An integrated Decision Support System (DSS) will be developed to provide managers and policymakers at many levels with the data they need to make informed decisions. The different users of
the system may require subtly different presentations of data to support their decision-making,
such that it may not be possible to create a single presentation that meets the needs of each user.
For the initial phase of the program, a single tool will be developed to meet a majority of each user’s
needs. As much as possible, the data serving system will be developed in such way that it can
support additional tailored tools which may be developed in future phases.
The initial decision support tool will show data in two primary ways: as raw data and as
synthesized information. At a raw data level, the DSS will expose the data produced by each data
source in a manner which is close to its native data format. Observation values from meteorological
sensors will be presented at geo-located points with minimal unit conversion and formatting.
Gridded satellite and model fields will be presented as graphical color-coded overlays on the map.
At a data synthesis level, the DSS will present additional views of the data which may further
facilitate decision-making. Observations may be presented in a spatial or temporal context which
conveys geographic extent or time trending. Gridded data may be shown in 2-dimensional slices
through 3 or 4 dimensional data, such as cross-sections. Fields may be composited and/or
interpreted to produce alerts over areas and times. The figures below show examples of DSS
developed by NCAR.
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The decision support tool will connect to data services over the internet. It will run on a standard
desktop computer with any of the leading contemporary operating systems, Windows, Macintosh,
and Linux. The tool will be accessible from anywhere on the internet, either as a Java application or
a browser-based web application. It may be restricted to specific users by IP address or login, as
determined. Tool performance will be dependent on the network speed.
2.3.5 Data Services
Data services will be run on the same host as the data repository. Services will be available over
the open internet to support access by the decision support system tool and to provide for future
use by other tools and systems. The proposed system will be initially developed and run
experimentally at NCAR, and will then be transferred to an operational system in Rwanda. The
Rwanda system will require staff with sufficient skill in network administration and system
configuration to maintain the operational system.
2.4 Training
While the decision support tools and hardware/software systems developed at NCAR are
important in themselves, the education and training of stakeholders and end users is essential
for the products to have their full impact. We typically invest considerable time and energy in
conducting workshops and tutorials, developing and delivering tailored instructional materials, and
bringing visitors to NCAR for collaborative visits. Each year hundreds of scientists from around the
world attend workshops at NCAR focused on use of the Weather Research and Forecasting (WRF)
model, the Community Climate System Model (CCSM), and the Model Evaluation Tools (MET).
Numerous other workshops bring scientists together to share their knowledge and develop new
research agendas in areas such as climate, weather and health; water resources; verification of
weather forecasts; and numerical weather prediction and data assimilation.
In terms of specific training anticipated and tailored for REMA and the Rwandan Meteorological
Office personnel, in Phase 1 it is broken down into three goals: (a) in the early period of
performance (PoP) of the consultancy, training will be provided on the basic technologies the
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contract will introduce to REMA; (b) at the end of the PoP, the goals will be to provide additional
training on new technologies introduced, revisiting the theory introduced in (a) but focusing on
operational and functional issues; and, (c) holding a workshop on proposed future phases of EWS
technologies to be introduced, which would include basic science tutorials along with dialogue with
Rwanda participants on local needs, tailoring, and expectations.
Regarding (c), it is worth noting again that the need to provide training on possible future EWS
technologies comes about because NCAR is proposing that REMA develop its EWS capability in
phases. In particular, this means in the future a strong dependence on advanced observational data
(i.e., radar, rain gauge, satellite products, etc). Trainings sessions on new technological
developments would be kept general enough that if REMA were to use a different institutional
consultant for latter EWS development, the materials would still be relevant.
An example of training provided to bridge from a “Phase 1” to a “Phase 2,” EWS capability would
include advanced short-term storm forecasting (termed nowcasting). A number of technologies
developed at NCAR would be relevant in this effort including TITAN, a storm tracking system that
obtain storm motion and storm growth trends; heuristic nowcasting (e.g., the NCAR
Autonowcaster), and statistical optimization (e.g., genetic algorithms, and “random forest”
methods) to predict the development and evolution of storms; blending of numerical model
forecasts with nowcasts, including model phase error correction, and techniques to combine all this
information into a single consistent forecast picture with uncertainty bounds. Techniques have also
been developed at NCAR to use satellite-based rainfall estimates in conjunction with global
numerical weather prediction results to produce timely, high-resolution 0-3 hr nowcasts of rainfall
amounts. This product was developed for use in remote (often oceanic) regions where surfacebased radar networks are not available. Techniques to blend these rainfall estimates with NWP
results for the purpose of extending the forecast period to 12 hr are underway now and could be
implemented as an extension to the 0-3 hr product, once the blended product is proven to have
adequate performance.
3. Work Plan
3.1 Assessment
Expert experience of key personnel: hydrology, nowcasting, instrumentation, decision support,
computer infrastructure, algorithm development, radar integration
3.1.1 Tasks
 Gishwati region infrastructure assessment – 2 days
 National infrastructure assessment: radar, DSS and dissemination –4 days
 Pre-project training on capacities and considerations – 2 days
3.1.2 Budget
Pre-trip planning and survey, 2 week trip (5 personnel), future technical development plan and
recommendations completed (17 person-weeks)
3.1.3 Timeline
Completion date: no later than 6 months from start of project
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3.2 Utilization of publicly-available large-scale datasets
Context: it is presumed at present that the only currently available data relevant for early warning
system deployment will exclusively come from publicly-available data sources, specifically: satellite
precipitation estimates, medium-range numerical weather prediction from global modelling
centers (e.g., NCEP, ECMWF, etc.)
The data sources are (1) satellite precipitation estimates (SPE), including the NOAA HydroEstimator, NASA TRMM, NOAA Cmorph; (2) any in-situ in-country rain gauges; and (3) freelyavailable GCM ensemble forecasts (NCEP, Thorpex-Tigge archived data)
3.2.1 Tasks
 GIS delineation of sub-catchments within the Gishwati region of the pilot program (1
person-wk)
 Processing of any-available raingauge and/or streamflow data sets (2 person-wk)
 Processing of past and current SPE over pilot study area (1 person-wk)
 Processing of past and current NWP over pilot study area (2 person-wk)
 Skill assessment of SPE as compared to any available rain gauge (1 person-wk)
 Skill assessment of NWP as compared to any available rain gauge or SPE (1 person-wk)
 Post-processing algorithm development of SPE and NWP to application (2 person-wk)
 Hydrologic modelling development and comparison studies (8 person-wk)
 Automation of SPE and NWP download and processing (2 person-wk)
 Automation of integrated data sets into rudimentary hydrologic variables and forecasts (2
person-wk)
 Assessment of storm climatologies and characteristics (6 person-wk)
3.2.2 Budget
 28 person-weeks
3.2.3 Timeline
Completion date: 12 months from start of the project.
3.3 Information Management and Decision Support Systems (DSS) for Weather and
Flood Forecasting
Context: consultant assumes that in-country data sources for DSS ingest will be available over openinternet or secure private network (VPN) in a WMO-approved format.
3.3.1 Tasks
 Display currently available satellite and numerical weather prediction products (4 personwk)
 Build capacity for future integration of met sensors, radar, derived variables (hydromet),
tasking entailing ingest, preprocessing and storage, data services for dissemination
o Radar (8 person- wk)
o Raingauge (6 person-wk)
o Streamflow measurements (6 person-wk)
 GIS situational awareness, including base maps and tools (4 person-wk)
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3.3.2 Budget
 38 person weeks
3.3.3 Timeline
 Information development for currently available products and DSS development: beginning
of POP ongoing
 In-country hydromet sensors: beginning once data are available
3.4 Training
3.4.1 Tasks
In-country training covering existing technologies contained within the scope of this proposal,
along with training on possible anticipated technologies relevant to EWS (a more in-depth
continuation of short lectures provided at beginning of consultancy).
3.4.2 Budget
1 week trip, 4 personnel, preparation, travel, and content delivery (4 X 2 wk = 8 person weeks)
3.4.3 Timeline
Training near the end of the project period of performance (18 month)
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