WORLD METEOROLOGICAL ORGANIZATION
EXECUTIVE COUNCIL
PANEL OF EXPERTS ON POLAR OBSERVATIONS,
RESEARCH AND SERVICES
Fourth session
EC-PORS-4/INF.13
Submitted by : Juhani Damski,
Kari Luojus and Jouini Pulliainen
.
Date: 21.II.2013
Lanzhou, China, 13-15 March 2013
AGENDA ITEM: 3.4
REVIEW OF EC-PORS ACTIVITY SINCE EC-PORS-3: SERVICES
EMERGING CRYOSPHERIC SERVICES
SUMMARY
ISSUES TO BE DISCUSSED:
emerging cryospheric services.
For information. This document provides information about
DECISIONS/ACTIONS: Discussions on possibilities to incorporate emerging cryosphere
services as described in this document in to the PORS framework.
EC-PORS-IV/INF.13, p. 2
1. EMERGING CRYOSPHERIC SERVICES
1.1
Hemispherical scale monitoring of cryosphere has been carried out using satellite
data for several decades. Operational monitoring of snow cover has been mainly restricted
to observations of snow cover extent using optical satellite data. Optical methods provide
information solely on spatial extent, lacking information on crucial parameters such as snow
depth (SD), snow water equivalent (SWE) and total water content of the snow pack, all
crucial variables for many operational end-users. A new methodology, combining satellitebased coarse resolution passive microwave radiometer data and ground-based synopobservations has allowed the operational near-real time monitoring of snow depth and
snow water equivalent on hemispherical scale in recent years.
1.2
A methodology developed at Finnish Meteorological Institute (FMI) combines a
forward emission model for snow covered ground in Bayesian assimilation scheme to
calculate maximum likelihood estimates of snow water equivalent and snow depth utilizing
satellite-based data in conjunction with ground-based Synoptic observations. The
methodology has been in operational use for several years providing daily near-real time
(NRT) maps of SD and SWE for the land areas of Northern Hemisphere (NH).
1.3
The potential end-users of the snow (SWE & SD) maps include numerical
weather prediction (NWP), hydrological forecasting including flood forecasting and water
storage optimization for hydro-power applications, avalanche warning centers, climate
monitoring institutes, environmental agencies, energy traders and road and railway
authorities.
1.4
There are several operational users for the NH SWE maps. A key national user is
the Finnish Environment Institute (SYKE), where the SWE data is used as an input for
hydrological forecasting system providing nation-wide information of current and future
water resources for flood monitoring and river runoff forecasting. The information from
SYKE is operationally utilized by Finnish hydropower companies in hydropower
optimization and flood prevention.
1.5
Joint Research Centre (JRC) of the European Commission operates the
“European Drought Observatory” (EDO) that produces European wide drought-relevant
information such as maps of indicators derived from different data sources (e.g.,
precipitation measurements, satellite measurements, modelled soil moisture content). FMI
produces a standardized snow pack indicator (SSPI) product for the EDO that is based on
the recently developed SWE mapping methodology. The SSPI is calculated as an anomaly
of current SWE conditions when compared with long-term historical SWE conditions. An
example map of the SSPI indicator is shown in Figure 1.
1.6
There are on-going studies for utilization of the FMI produced SWE maps for
numerical weather prediction by the FMI and Swedish Meteorological and Hydrological
Institute (SMHI). Environment Canada (EC) is investigating the utilization of the SWE maps
for their operational daily CMC snow depth analysis system, which estimates terrestrial
snow conditions for Northern Hemisphere used as an input for NWP at EC.
EC-PORS-IV/INF.13, p. 3
1.7
There is a joint European Commission -funded project including FMI and
Icelandic Meteorological Institute to utilize the SWE maps in avalanche forecasting
activities within Nordic countries.
1.8
In addition to the near-real time processing of SWE maps, a daily time-series of
Northern Hemisphere SWE has been produced spanning more than 30-years within the
European Space Agency (ESA) Data User Element (DUE) GlobSnow project. The
GlobSnow SWE data record is based on the time-series of measurements by two different
space-borne passive radiometers (SMMR and SSM/I) combined with ground-based
weather station observations. The time series spans from 1980 to present day at a spatial
resolution of approximately 25 km.
1.9
The GlobSnow long-term snow water equivalent data record can be used to
reconstruct the snow conditions for the Hindu Kush-Himalayan region also called as the
third pole for the past 30 years. This reconstruction, along with a near-real time production
of SWE maps for the region, could be utilized to derive an estimate of
anomalies/differences on observed every-day snow conditions when compared with
conditions people are used to in the past.
1.10
As the GlobSnow data-series is based on coarse resolution passive microwave
radiometer observations, its accuracy is limited for mountainous domains, such as the Third
pole region. However, on a larger scale, it is indicative of the changes observed within the
region.
2. Demonstration of drought/snow pack indicator for the third pole region
A demonstration service of drought indicator calculation for a sub-domain of the Third pole
region in collaboration with interested partners within the region can be provided. Such a
demonstration project should include the following work:
 A calculation of the snow pack indicator product in NRT for a given selected region
 A partner/user evaluation of the snow pack indicator product, based on the
hemispherical SWE mapping, providing feedback and quality assessment of the product
 If feasible, inclusion of additional ground-based weather station data for inclusion on the
SWE processing chain, and
 An assessment of the performance improvement due to increased number of groundbased snow depth data.
EC-PORS-IV/INF.13, p. 4
3. FIGURES
Figure 1. Example of the SSPI product for European domain for the winter 2005-2006. The
example is shown with on a monthly basis, calculation is possible on a daily to a weekly and
monthly interval.