Kenya Meteorological Service visit to SERVIR brings teamwork to the forefront
Some new international guests were seen around the SERVIR coordination office in Huntsville, Alabama, in early June 2015. John Mungai and Vincent Sakwa, weather forecasters from the Kenya Meteorological Service (KMS), were hard at work alongside NASA/SPoRT research meteorologist Jonathan Case. SPoRT, the Short-term Prediction Research and Transition center, is working with SERVIR to help KMS incorporate new satellite-based data into their weather prediction model. The activity is part of a Small Scale Application that KMS has with the Regional Center for Mapping of Resources for Development (RCMRD), the SERVIR-Eastern and Southern Africa (E&SA) host organization.
KMS keeps the Kenyan public informed, issuing warnings about extreme weather events. The agency runs the WRF (Weather Research and Forecasting) model in real-time to support daily forecast operations.
“You have to remember that working with models is always a continuous improvement,” Mungai notes. “Our WRF is the best we’ve got, but we know it could be better.”
Day in and day out at SERVIR, Case, Mungai, and Sakwa flipped through binders and navigated through meticulous code as the pair of weather forecasters learned to use new tools for better forecasting in the Kenya and Eastern Africa region.
Convective activity is something that forecasters like Mungai and Sakwa have to accurately predict to inform the public. Thunderstorms provide needed precipitation but are known all too well for causing floods and lightning. Within hours of the first heavy rainfall, river banks can burst, driving families from their homes without time to protect their valuables -- and their lives. Lightning strikes can kill instantly; cloud-to-ground bolts can contain up to one billion volts of electricity. Many lives are lost to lightning strikes each year in Africa.
The collaboration between KMS, SERVIR, and SPoRT aims not only to enhance KMS’s numerical weather prediction capabilities, but also to increase the accuracy of weather forecasts in the region. SERVIR partnered with SPoRT to help KMS incorporate NASA satellite data from the Land Information Systems (LIS) and Visible Infrared Imager Radiometer Suite (VIIRS) into their WRF. (These products are described in the Notes section below.)
Case also worked with Mungai and Sakwa to compare the new model results with real-time observations, and to produce scores to verify the WRF forecasts’ accuracy. These differences are measured by the Model Evaluation Tools (MET) verification software, which Mungai and Sakwa learned to use during the course of the training.
“The MET verification will get the real time data; then a statistical analysis will be done to see how accurate these additions are in our models,” Sakwa explains. “From day to day it doesn’t seem like much of a difference, but long term you can get a bigger picture of how helpful the addition of land observations are in our forecasts.”
The WRF improvements are ‘trickling down’, benefiting several applications that depend on its projections. For example, the 72-hr Quantitative Precipitation Forecast (QPF) produced by the WRF is used in SERVIR’s Coupled Routing and Excess Storage (CREST) hydrological model and in SERVIR-E&SA/RCMRD frost forecasting.* SERVIR-E&SA/RCMRD actively runs the CREST model in Eastern Africa to simulate streamflow to forecast the likelihood of floods.
All of this teamwork has created a win-win situation for KMS, the SERVIR team, and, most importantly, the people of East Africa.
“Warning and relocating people along river banks is important, and now doable with more precise projections,” says Mungai. “The incorporation of this new data into our model is a valued addition, serves a research point, and gives us the opportunity to collaborate with great projects like SPoRT and SERVIR.”
Notes:
- *Sakwa notes that other beneficiaries from the enhancement made to the KMS WRF weather forecasts include the aviation industry, agricultural sector, tourism, and Severe Weather Demonstration Project –East Africa.
- LIS is a NASA-developed land-surface modeling and data assimilation framework. As a tool to produce land surface initial conditions for a forecast, it integrates satellite- and ground-based observational data products to better represent the characteristics of the Earth’s surface. This ground state, which includes variables such as temperature fluxes and soil moisture variations, can have a large influence on the transport of heat and moisture to the atmosphere. With more information on this transport, the WRF model has the ability to more accurately predict where and when rain will occur.
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Information on ground vegetation coverage specifically comes from the Green Vegetation Fraction (GVF) derived from VIIRS. This product provides information concerning the connection between transpiration (evaporation off of plants) and moisture in the atmosphere. GVF is generated by NOAA’s National Environmental Satellite, Data, and Information Service (NESDIS) (https://data.noaa.gov/dataset/nesdis-viirs-green-vegetation-fraction).
This article was written by Rachel Gaal, a technical writing intern for SERVIR through the NASA-MSFC Summer Intern program. She is a student at University of Oklahoma working toward earning a B.S. in Meteorology with a Mathematics minor.