2017: Weather Extremes and Record Losses

From left to right: Katia (Tropical Storm), Hurricane Irma and Jose (not yet a hurricane).  NASA/NOAA GOES 

The year 2017 was the second warmest year since the start of measurements around 1880, and the warmest year without El Niño effect. Losses caused by extreme weather events broke all records. The overall losses due to natural catastrophes were estimated to be $330bn, of which $267bn (81%) was caused by meteorological catastrophies.

The climate analysis of the Copernicus Climate Change Service (C3S), see figure below, shows that the global average temperature of 2017 is 14.7°C. This is 1.2°C above pre-industrial reference temperature, and 0.5°C above the average of the years 1981-2010.

The hurricane season in the Atlantic was very active and set some remarkable records. Hurricane Harvey caused a record amount of precipitation above Texas. Irma was the hurricane with the worldwide longest episode of category 5. The hurricane Ophelia reached Europe and was the most eastern hurricane since the start of the satellite era.

Other extremes on the list of 2017 are the summer heat wave and wildfires in Europe. Nepal, India and Bangladesh received such severe monsoon rains, that 2,700 people lost their lives due in floods. Drought continued in (parts of) East Africa and a landslide in Sierra Leone killed roughly 500 people. In Peru and Colombia, heavy rains caused landslides and floods resulting in hundreds of casualties.

The weather-related losses of 2017 fit in a trend of increasing losses due to extreme weather. The last years weather again shows us the importance of studying the changing weather pattern and improving warning systems to limit the potential losses and human sorrow.

Further reading:

ECMWF: 2017 extends exceptionally warm period

KNMI: Atlantic Hurrucane Season 2017 very active (Dutch)

Acclimatise: 2017 the year in extreme weather

Munich Re: 2017 year in figures

Focus on African agriculture in 2018

The new year prospects for new chances in African small-scale agribusiness. The Weather Impact team celebrated the end of the year 2017 in Ethiopia. Together with Wageningen Environmental Research, a workshop on agro-meteo services for small-scale Ethiopian farmers was organised. We came together with 20 Ethiopian meteorological and agronomical experts, to discuss the best methods and partnerships for a reliable and financially sustainable service in Ethiopia. It was decided that the mobile text message with a localized weather forecast, provided in 3 indigenous languages, will be continued in 2018 with at least a doubled number of users.

In Kenya, ten-thousands of farmers will be reached with agro-meteo advisory in the CropMon project. In South-Africa, a Mobile farm-advisory app for extension officers will be released in the context of the R4A project. Furthermore, new projects will start in various African countries.

In March 2018, Weather Impact organises a symposium on “Weather information as success factor in local African agriculture” to celebrate our 3rd anniversary and share the successes with our partners.

The Weather impact team wishes you a healthy and successful 2018!

Suitability-maps for Oil Palm cultivation in Eastern Democratic Republic of Congo

Weather Impact is doing an assessment of the climate of the Eastern part of the Democratic Republic of Congo. Goal of the assessment is to analyse the suitability of the area to cultivate Oil Palm (Elaeis Guineensis). Oil palm is a humid, tropical crop; it thrives best in areas with minimum temperatures above 20°C and an annual rainfall of more than 1800 mm, preferably equally spread over the year. The Congo Basin one of the three largest convective rainfall regions on the planet, under researched because of a severe lack of meteorological observations over the basin. Nevertheless, with support of satellite data and reanalysis datasets it is possible to get a view on local climate and its variability over time. Based on our data, value-adding maps are developed to inform about the suitability of certain areas to cultivate Oil Palm.

On the left-hand side of the figure below the mean annual rainfall over Eastern Congo, Burundi, Rwanda and the great lakes is shown. The right-hand side of the figure shows a suitability map, indicating which places are best suited for growing oil palm based on rainfall. The colours on the map indicate the percentage of years with sufficient rainfall for optimal production of palm oil, darker colours mean the area is better suitable. Local differences in rainfall can be detected, explanatory factors for these differences are local topography, land use and the presence of lakes.

Doing climate assessments in remote areas with a lack of observed climate data is challenging. Performance and reliability of data is hard to assess with a scarcity of ground-truthing. Satellite data and numerical models are an important source of information for these areas and although performance evaluation is constrained, it provides the opportunity to get an impression of local weather and climate variability. Tailored studies as presented here can help understanding the impact of local weather and climate change on the livelihood and food security of the people living there.

 

 

 

 

 

 

 

 

 

 

References:

  • Data is based on the CHIRPS datasource
  • R. Washington, R. James, H. Pearce, W. M. Pokam and W. Moufouma-Okia, “Congo Basin rainfall climatology: can we believe the climate models?”, Philosophical transactions of the Royal Society of London. Series B, Biological sciences, vol. 368, p. 1-7, 2013.
  • M. Carr, “The Water Relations and Irrigation Requirements of Oil Palm (Elaeis Guineensis): a Review”, Experimental Agriculture, vol. 47, pp. 629-652, 2011.

Weather forecasts for sesame farmers in Ethiopia

One of the main problems for Ethiopian sesame farmers is a general lack of reliable weather forecasts. The lack of forecasting information becomes more and more challenging for the sesame farmers as the variability in weather increases, which results in an unpredictable and highly variable sesame yield.  To address this challenge, Weather Impact provides local weather forecasts in two local languages direct to the sesame farmers. This activity is part of the CommonSense project, and to be able to send the weather forecasts, we work closely together with the Benefit-Sesame Business Network (SBN), Apposit and the Ethiopian National Meteorology Agency (NMA). In a pilot study, 1500 farmers received a weather forecast by SMS twice a week. With the help of our weather information, sesame farmers and agricultural professionals are able to strengthen their resilience to weather variability.

A user survey was conducted under a representative group of sesame farmers and agricultural experts. The results of this survey shows very encouraging results. Rainfall forecasts are, compared to temperature and wind, the most important source of information for the farmers. Of the interviewed farmers, 96% finds the short-term rainfall forecasts being (close to) very accurate. Most of the farmers stress that the information should be provided in an easy-to understand message.  Weather indications such as ‘degrees Celsius’ and ‘millimeters rainfall’ are sometimes not well understood and could be phrased in a more intuitive manner.

Users were asked what they could accomplish or change in their farming practice by using the rainfall forecasts. All interviewed farmers said that after receiving the rainfall forecasts, they were able to better plan their farm activities. These farm activities include decisions on the time of sowing, weeding and harvesting, the planning and adjustments of hired labor, applications of top dressing, fertilizer or pesticide and protections from flooding. The weather information supported the farmers to mitigate the risk of making the less optimal decisions and supports them to increase their profit by increasing the chances of a better yield. The interviewed farmers and agronomists believe that the weather forecast service should be scaled-up to a larger number of farmers, so more farmers have the opportunity to improve their management.

The CommonSense project and the partners in this pilot are currently exploring different opportunities to scale up the weather service in Ethiopia. If you are interested to collaborate with us, please do not hesitate to contact us at info@weatherimpact.com.

 

 

 

 

 

 

Results of the user survey among sesame farmers receiving the weather forecasting service in Ethiopia. Figures are created by Allard de Wit, Wageningen Environmental Research, partner in CommonSense.

This text is based on the report on the weather service pilot evaluation, written by Melisew Misker Belay, agronomist at Benefit Sesame Business Network, Ethiopia.  In the figure above you see Melisew Misker Belay (SBN) evaluating the weather forecasting pilot with one of the users. Interested in more information? Please download the user story of Ato Gureshaw Yilma here.

 

La Niña 2017: what are the impacts on East African weather?

For agricultural practices it is very convenient to be able to anticipate on the coming seasons’ weather. Especially in Africa, where most of agriculture is rain-fed, it is very helpful to know in advance if it will be a relatively wet or dry season, so farmers can choose the optimal crop or variety. Forecasting the weather on seasonal timescales is more difficult than forecasting the next few days. One of the reasons for this is that seasonal patterns depend on the status of the global climate system, including the oceans. An example of a well-known seasonal pattern is the El Niño phenomenon. El Niño is an event of unusual warming of the Equatorial Eastern Pacific Ocean in December and it has effects on the weather around the globe, especially on rainfall patterns in the tropics. El Niño is the warm phase of a larger phenomenon called the El Niño Southern Oscillation (ENSO). The opposite of El Niño is called a La Niña, which describes a cooling in the Equatorial Pacific Ocean.

In 2015 we have seen a big El Niño event, that affected agricultural production in many places in the world. The system is now reverting to a La Niña. In figure 1 we are looking at seasonal (long range) forecasts for sea surface temperature made by the European Centre for Medium Range Weather Forecasting (ECMWF). All the ensemble members of the model (red lines) are negative, indicating that a solid La Niña is forecasted the coming half year. Unfortunately the models did not predict this La Niña well ahead; half a year ago they even had a tendency of predicting another El Niño this year. The situation demonstrates how difficult it still is to predict the occurrence of El Niño long in advance.

Figure 1: El-Niño forecast of an ‘ensemble’ of models for the coming months in red, observations in the black dotted line. Negative values indicate La Niña. The figure is created by the ECMWF and can be downloaded here.

The current rainfall patterns show that in most parts of Eastern Africa, the rainfall season of summer and autumn 2017 was not as dry as in 2015 and 2016. Figure 2 shows the Standardised Precipitation Index for rainfall in the last 90 days, based on CHIRPS rainfall data. Blue colours indicate above average rainfall. Note the enhanced rainfall in Western East Africa and large parts of Ethiopia, that is likely to result in improved yields for most of farmers rains in Kenya, Uganda, Ethiopia. Other countries (especially Somalia and Eastern Democratic Republic of Congo) experienced reduced rainfall and a poorly performing season.

Figure 2: Standardised Precipitation Values of 90 days accumulated rainfall over Eastern Africa on 20th October 2017. Blue colours indicate above-average rainfall. This figure is created by Weather Impact based on CHIRPS data.

 

What is the expected rainfall for the coming months in East Africa and do we see an effect of the forecasted La Niña?

Historically, La Niña correlates with below-average rainfall in the months December to February in parts of Eastern Africa, whereas Southern Africa is wetter and cooler than normal. The seasonal precipitation forecasts of ECMWF for the next three months (November, December, January) are shown in Figure 3. Yellow and brown colors indicate that it is more likely to be a dry season than a wet season, whereas the green colors indicate that it will be more likely a wet season than a dry season. The forecasts show above-average rainfall over Tanzania, Malawi, Mozambique and Madagascar and not a clear defined ‘dryer than normal’ area; a little different for the historically known fingerprint of La Niña. Note that the figure only shows the most likely scenario for this year.

Time will learn if these scenarios were right. It is our goal to explain the remaining uncertainties as good as possible to the stakeholders of reliable weather forecasts, among which farmers, to support optimal decision making in the agricultural sector.

 

Figure 3: ECMWF seasonal forecast for rainfall in the months October, November, December. The colors indicate the probability of the most likely category of precipitation. The figure is created by the ECMWF and can be downloaded here.

CCCA initiative

The Netherlands Consortium on Climate Change Adaptation  is a broad coalition of Dutch knowledge centres, dedicated to providing multisectoral, integrated, practical knowledge and expertise on climate change adaptation. Take a look at the website of this initiative, or read the article in Volkskrant (Dutch). Weather Impact is associated partner in the CCCA initiative.

 

Vacancies for internships

 

 

 

 

 

 

 

 

 

 

 

 

 

Weather Impact has currently three vacancies open for internships. We are looking for enthusiastic students from different disciplines to expand our team.

Statistical forecasting of convective weather patterns

Develop a statistical method to predict convective weather patterns from output of numerical weather prediction models.

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Developing a marketing approach for weather products in Africa

Scan the African market and work out which weather-information-products have the best market opportunities for Weather Impact.

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(NL) Marktverkenning toegepaste weerinformatie voor Agrifood Business

Creeër inzicht in de problemen en uitdagingen van de klanten van Weather Impact op het snijvlak van voedsel, landbouw en weer.

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Honorable Mention Winner World Bank Big Data Innovation Challenge

The World Bank launched a global call to find big data solutions that addressed issues in the critical challenge of Food security and nutrition. The goal was to help better understand the impacts of climate change and positively influences decisions by using big data effectively. Weather Impact has taken up this challenge and created the innovate Banana Network. With our application we are recognized as Honorable Mention Winner by the World Bank.

Banana production is highly sensitive to the impact of global warming. The Banana Network is an artificial neural network trained by big data on global weather and banana production. It provides local producers reliable information on their estimated yields and can be used to assess productivity in a warming climate. The model uses open source big data, is scalable and has unlimited training capacity. Knowledge from the Banana Network will help farmers and producers to (1) plan investments more effectively, (2) receive a fair price for their crops, and (3) take early action in the event of a potential disaster.

CropMon video pitch

Watch here our new video-pitch about CropMon. The CropMon service in Kenya provides local information on weather forecasts, current crop growth and farm management practices. Weather Impact delivers tailored weather forecasts and monitors current weather conditions. Farmers receive text messages with weather forecasts and farming advice on a regular basis. In addition, innovative smartphone- and web applications are developed for farmer organisations and other stakeholders.