Seasonal forecasting and the predictability of the rainy and dry seasons for Peru, Tanzania and India

2020 ◽  
Author(s):  
Thomas Möller ◽  
Lydia Gates
Keyword(s):  
East Africa ◽  
Weather Conditions ◽  
Forecast Model ◽  
Seasonal Forecast ◽  
Climate Impact ◽  
Seasonal Forecasting ◽  
Mean Deviation ◽  
Local Agriculture ◽  
Forecast Models

<p>With seasonal forecast models we investigate whether it is possible to give the people in Tanzania, Peru and India time to adapt and prepare to different weather conditions. In recent years, these countries have repeatedly experienced devastating droughts or floods, such as in East Africa in November 2019.</p><p>Under the framework of the research project EPICC (East Africa Peru India Climate Capacities) supported by the BMU (Federal Ministry for the Environment, Nature Conservation and Nuclear Safety), we aim to set up a seasonal forecast system. The goal is to make the data useful for the hydrologists at the project partner from PIK (Potsdam Institute for Climate Impact Research) for integration in a tool for adaption in local agriculture in the affected countries (India, Peru and Tanzania). In this study, we validate a number of variables of predicted anomalies in seasonal forecast models as well as of a multimodel product.</p><p>There are different methods of seasonal predictability, based on slow variations of boundary conditions, coupled ocean-atmosphere model simulations as well as the concept of ensembles, multi-model ensembles and uncertainties. The focus in this study is on the intercomparison of the single models and the multimodel in a forecast range between 1 and 6 months. In particular, we investigate three-month mean deviation from the long-term mean. It is important for the population (especially for the agriculture industry) in the focus region to know whether in a certain period (rainy season, dry season, El Nino etc.) the next 3 months will be colder, warmer, drier or even wetter compared to the long-term mean.</p><p>Due to the fact, that various seasonal forecasting models perform differently, it is the challenge, to find the best fitting seasonal forecast model for each of the affected countries.</p>

Evaluation of the European Seasonal forecast Models for hydrological forecasting for improved water management in Africa

2020 ◽  
Author(s):  
Solomon Hailu Gebrechorkos ◽  
Justin Sheffield
Keyword(s):  
Climate Change ◽  
Early Warning ◽  
Seasonal Forecast ◽  
Infiltration Capacity ◽  
Adaptation Measures ◽  
Hydrological Forecasting ◽  
Coarse Spatial Resolution ◽  
Forecast Models ◽  
Expert Team

<p>Extreme drought and floods have a large societal impact if not appropriately monitored and if mitigation/adaptation measures are not developed and modified based on early warning of these events. Currently, there are only a few global seasonal forecast models available with a high temporal (e.g., daily) but coarse spatial resolution (~1°) that can provide early warning operationally. Application of these forecast models for long-term (up to 6-7 months) hydrological forecasting first requires evaluation of their skill against observed data. In this study, five European seasonal forecast models; Copernicus Climate Change Service (ECMWF), UK Met Office, Météo France, Deutscher Wetterdienst (DWD), and Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), are used. The Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Princeton Global Forcing (PGF) available at high spatial and temporal resolution are used as a reference dataset for precipitation and maximum and minimum temperature, respectively. Multiple methods such as correlation, percentage of bias and root mean square error and rainfall onset and cessation are used to evaluate the skill of individual models on daily, monthly, seasonal, and climatological periods. In addition, extreme indices (e.g., consecutive dry and wet days) developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) are used. Finally, a bias-corrected multi-model weighted ensemble forecast is developed as input into a global hydrological model (Variable Infiltration Capacity (VIC)) for seasonal hydrological forecasting in Africa.</p>

scholarly journals GREY FORECASTING MODEL IMPLEMENTATION FOR FORECAST OF CAPTURED FISHERIES PRODUCTION

Kursor ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 169
Author(s):  
Muhammad Shodiq
Keyword(s):  
Weather Conditions ◽  
Forecast Model ◽  
Fishing Ground ◽  
Forecasting Model ◽  
Fishing Gear ◽  
Short Term ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Forecast Models

The increasing need for fish causes problems related to production in the fisheries sector. In fisheries production all information related to (fishing ground) is well known, but on the other hand it is not easy to predict the amount of production due to unclear information. This is also related to the number of ships that make trips, the length (time) of the trip, the type of fishing gear, weather conditions, the quality of human resources, natural environmental factors, and others. The purpose of this study is to apply Grey forecasting model or GM (1,1) to predict fisheries production. Grey forecasting models are used to build forecast models with limited amounts of data with short-term forecasts that will produce accurate forecasts. This study employs the data of captured fish from 2010 to 2018 to analyze calculations using the GM model (1,1). The results showed that the Grey forecasting model or GM (1.1) produced accurate forecasts with an ARPE error value of 9.60% or the accuracy of the forecast model reached 90.39%.

scholarly journals Operational seasonal forecasting of crop performance

2005 ◽  
Vol 360 (1463) ◽  
pp. 2109-2124 ◽  
Author(s):  
Roger C Stone ◽  
Holger Meinke
Keyword(s):  
Decision Making ◽  
Agricultural Production ◽  
Crop Production ◽  
Prediction Models ◽  
Weather Prediction ◽  
Seasonal Forecast ◽  
Seasonal Forecasting ◽  
Operational Management ◽  
Crop Performance

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.

scholarly journals Epidemic malaria and warmer temperatures in recent decades in an East African highland

2010 ◽  
Vol 278 (1712) ◽  
pp. 1661-1669 ◽  
Author(s):  
David Alonso ◽  
Menno J. Bouma ◽  
Mercedes Pascual
Keyword(s):  
Climate Change ◽  
East Africa ◽  
Nonlinear Response ◽  
Climate Change Impacts ◽  
Human Model ◽  
Recent Past ◽  
East African ◽  
Highly Nonlinear ◽  
The Impact

Climate change impacts on malaria are typically assessed with scenarios for the long-term future. Here we focus instead on the recent past (1970–2003) to address whether warmer temperatures have already increased the incidence of malaria in a highland region of East Africa. Our analyses rely on a new coupled mosquito–human model of malaria, which we use to compare projected disease levels with and without the observed temperature trend. Predicted malaria cases exhibit a highly nonlinear response to warming, with a significant increase from the 1970s to the 1990s, although typical epidemic sizes are below those observed. These findings suggest that climate change has already played an important role in the exacerbation of malaria in this region. As the observed changes in malaria are even larger than those predicted by our model, other factors previously suggested to explain all of the increase in malaria may be enhancing the impact of climate change.

scholarly journals REVIEW OF SEASONAL CLIMATE FORECASTING FOR AGRICULTURE IN SUB-SAHARAN AFRICA

2011 ◽  
Vol 47 (2) ◽  
pp. 205-240 ◽  
Author(s):  
JAMES W. HANSEN ◽  
SIMON J. MASON ◽  
LIQIANG SUN ◽  
ARAME TALL
Keyword(s):  
Smallholder Farmers ◽  
Seasonal Forecast ◽  
Sub Saharan Africa ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Seasonal Climate ◽  
Climate Forecasting ◽  
Probabilistic Forecasts ◽  
Sub Saharan

SUMMARYWe review the use and value of seasonal climate forecasting for agriculture in sub-Saharan Africa (SSA), with a view to understanding and exploiting opportunities to realize more of its potential benefits. Interaction between the atmosphere and underlying oceans provides the basis for probabilistic forecasts of climate conditions at a seasonal lead-time, including during cropping seasons in parts of SSA. Regional climate outlook forums (RCOF) and national meteorological services (NMS) have been at the forefront of efforts to provide forecast information for agriculture. A survey showed that African NMS often go well beyond the RCOF process to improve seasonal forecast information and disseminate it to the agricultural sector. Evidence from a combination of understanding of how climatic uncertainty impacts agriculture, model-based ex-ante analyses, subjective expressions of demand or value, and the few well-documented evaluations of actual use and resulting benefit suggests that seasonal forecasts may have considerable potential to improve agricultural management and rural livelihoods. However, constraints related to legitimacy, salience, access, understanding, capacity to respond and data scarcity have so far limited the widespread use and benefit from seasonal prediction among smallholder farmers. Those constraints that reflect inadequate information products, policies or institutional process can potentially be overcome. Additional opportunities to benefit rural communities come from expanding the use of seasonal forecast information for coordinating input and credit supply, food crisis management, trade and agricultural insurance. The surge of activity surrounding seasonal forecasting in SSA following the 1997/98 El Niño has waned in recent years, but emerging initiatives, such as the Global Framework for Climate Services and ClimDev-Africa, are poised to reinvigorate support for seasonal forecast information services for agriculture. We conclude with a discussion of institutional and policy changes that we believe will greatly enhance the benefits of seasonal forecasting to agriculture in SSA.

Development of a seasonal forecast model for Kuwait winter precipitation

2001 ◽  
Vol 48 (2) ◽  
pp. 233-242 ◽  
Author(s):  
H.A. Nasrallah ◽  
R.C. Balling ◽  
N.J. Selover ◽  
R.S. Vose
Keyword(s):  
Forecast Model ◽  
Winter Precipitation ◽  
Seasonal Forecast

scholarly journals Dynamical mechanisms linking Indian monsoon precipitation and the circumglobal teleconnection

2021 ◽  
Author(s):  
Jonathan D. Beverley ◽  
Steven J. Woolnough ◽  
Laura H. Baker ◽  
Stephanie J. Johnson ◽  
Antje Weisheimer ◽  
...  
Keyword(s):  
North America ◽  
Northern Hemisphere ◽  
Rossby Waves ◽  
Wave Train ◽  
Forecast Model ◽  
Seasonal Forecast ◽  
The North ◽  
Circumglobal Teleconnection ◽  
Rossby Wave Train ◽  
Ecmwf Model

AbstractThe circumglobal teleconnection (CGT) is an important mode of circulation variability, with an influence across many parts of the northern hemisphere. Here, we examine the excitation mechanisms of the CGT in the ECMWF seasonal forecast model, and the relationship between the Indian summer monsoon (ISM), the CGT and the extratropical northern hemisphere circulation. Results from relaxation experiments, in which the model is corrected to reanalysis in specific regions, suggest that errors over northwest Europe are more important in inhibiting the model skill at representing the CGT, in addition to northern hemisphere skill more widely, than west-central Asia and the ISM region, although the link between ISM precipitation and the extratropical circulation is weak in all experiments. Thermal forcing experiments in the ECMWF model, in which a heating is applied over India, suggest that the ISM does force an extratropical Rossby wave train, with upper tropospheric anticyclonic anomalies over east Asia, the North Pacific and North America associated with increased ISM heating. However, this eastward-propagating branch of the wave train does not project into Europe, and the response there occurs largely through westward-propagating Rossby waves. Results from barotropic model experiments show a response that is highly consistent with the seasonal forecast model, with similar eastward- and westward-propagating Rossby waves. This westward-propagating response is shown to be important in the downstream reinforcement of the wave train between Asia and North America.

scholarly journals Does afforestation deteriorate haze pollution in Beijing–Tianjin–Hebei (BTH), China?

2018 ◽  
Author(s):  
Xin Long ◽  
Naifang Bei ◽  
Jiarui Wu ◽  
Xia Li ◽  
Tian Feng ◽  
...  
Keyword(s):  
Forest Cover ◽  
Fine Particles ◽  
Control Strategies ◽  
Surface Wind ◽  
Forecast Model ◽  
Surface Wind Speed ◽  
Satellite Measurements ◽  
Haze Pollution ◽  
The Impact

Abstract. Although aggressive emission control strategies have been implemented recently in the Beijing–Tianjin–Hebei area (BTH), China, pervasive and persistent haze still frequently engulfs the region during wintertime. Afforestation in BTH, primarily concentrated in the Taihang and Yanshan Mountains, has constituted one of the controversial factors exacerbating the haze pollution due to its slowdown of the surface wind speed. We report here an increasing trend of forest cover in BTH during 2001–2013 based on long-term satellite measurements and the impact of the afforestation on the fine particles (PM2.5) level. Simulations using the Weather Research and Forecast model with chemistry reveal that the afforestation in BTH since 2001 generally deteriorates the haze pollution in BTH to some degree, enhancing PM2.5 concentrations by up to 6 % on average. Complete afforestation or deforestation in the Taihang and Yanshan Mountains would increase or decrease the PM2.5 level within 15 % in BTH. Our model results also suggest that implementing a large ventilation corridor system would not be effective or beneficial to mitigate the haze pollution in Beijing.

scholarly journals How Can Existing Ground-Based Profiling Instruments Improve European Weather Forecasts?

2019 ◽  
Vol 100 (4) ◽  
pp. 605-619 ◽  
Author(s):  
A. J. Illingworth ◽  
D. Cimini ◽  
A. Haefele ◽  
M. Haeffelin ◽  
M. Hervo ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Positive Impact ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Time Data ◽  
Weather Forecasts ◽  
Forecast Models ◽  
Wind Lidar ◽  
Microwave Radiometers

Abstract To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.

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