scholarly journals Evaluation of Coupled Model Forecasts of Ethiopian Highlands Summer Climate

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mark R. Jury
Keyword(s):  
Crop Yields ◽  
Coupled Model ◽  
Maximum Temperature ◽  
Seasonal Forecasts ◽  
Early Season ◽  
Ethiopian Highlands ◽  
Climate Forecasting ◽  
Medium Range Forecast ◽  
Medium Range ◽  
Better Than

This study evaluates seasonal forecasts of rainfall and maximum temperature across the Ethiopian highlands from coupled ensemble models in the period 1981–2006, by comparison with gridded observational products (NMA + GPCC/CRU3). Early season forecasts from the coupled forecast system (CFS) are steadier than European community medium range forecast (ECMWF). CFS and ECMWF April forecasts of June–August (JJA) rainfall achieve significant fit (r2=0.27, 0.25, resp.), but ECMWF forecasts tend to have a narrow range with drought underpredicted. Early season forecasts of JJA maximum temperature are weak in both models; hence ability to predict water resource gains may be better than losses. One aim of seasonal climate forecasting is to ensure that crop yields keep pace with Ethiopia’s growing population. Farmers using prediction technology are better informed to avoid risk in dry years and generate surplus in wet years.

scholarly journals Medium-Range, Monthly, and Seasonal Prediction for Europe and the Use of Forecast Information

2006 ◽  
Vol 19 (23) ◽  
pp. 6025-6046 ◽  
Author(s):  
Mark J. Rodwell ◽  
Francisco J. Doblas-Reyes
Keyword(s):  
Coupled Model ◽  
Seasonal Prediction ◽  
Model Potential ◽  
Mitigation Strategies ◽  
General Level ◽  
Seasonal Forecasts ◽  
Seasonal Predictability ◽  
Potential Predictability ◽  
Medium Range ◽  
The Impact

Abstract Operational probabilistic (ensemble) forecasts made at ECMWF during the European summer heat wave of 2003 indicate significant skill on medium (3–10 day) and monthly (10–30 day) time scales. A more general “unified” analysis of many medium-range, monthly, and seasonal forecasts confirms a high degree of probabilistic forecast skill for European temperatures over the first month. The unified analysis also identifies seasonal predictability for Europe, which is not yet realized in seasonal forecasts. Interestingly, the initial atmospheric state appears to be important even for month 2 of a coupled forecast. Seasonal coupled model forecasts capture the general level of observed European deterministic predictability associated with the persistence of anomalies. A review is made of the possibilities to improve seasonal forecasts. This includes multimodel and probabilistic techniques and the potential for “windows of opportunity” where better representation of the effects of boundary conditions (e.g., sea surface temperature and soil moisture) may improve forecasts. “Perfect coupled model” potential predictability estimates are sensitive to the coupled model used and so it is not yet possible to estimate ultimate levels of seasonal predictability. The impact of forecast information on different users with different mitigation strategies (i.e., ways of coping with a weather or climate event) is investigated. The importance of using forecast information to reduce volatility as well as reducing the expected expense is highlighted. The possibility that weather forecasts can affect the cost of mitigating actions is considered. The simplified analysis leads to different conclusions about the usefulness of forecasts that could guide decisions about the development of “end-to-end” (forecast-to-user decision) systems.

Application of the NCEP Ensemble Prediction System to Medium-Range Forecasting in South Africa: New Products, Benefits, and Challenges

2007 ◽  
Vol 22 (1) ◽  
pp. 18-35 ◽  
Author(s):  
Warren J. Tennant ◽  
Zoltan Toth ◽  
Kevin J. Rae
Keyword(s):  
South Africa ◽  
Weather Conditions ◽  
Ensemble Prediction ◽  
Maximum Temperature ◽  
Ensemble Prediction System ◽  
Medium Range Forecast ◽  
Medium Range ◽  
Probability Forecasts ◽  
Environmental Prediction ◽  
Probability Density Distributions

Abstract The National Centers for Environmental Prediction (NCEP) Ensemble Forecasting System (EFS) is used operationally in South Africa for medium-range forecasts up to 14 days ahead. The use of model-generated probability forecasts has a clear benefit in the skill of the 1–7-day forecasts. This is seen in the forecast probability distribution being more successful in spanning the observed space than a single deterministic forecast and, thus, substantially reducing the instances of missed events in the forecast. In addition, the probability forecasts generated using the EFS are particularly useful in estimating confidence in forecasts. During the second week of the forecast the EFS is used as a heads-up for possible synoptic-scale events and also for predicting average weather conditions and probability density distributions of some elements such as maximum temperature and wind. This paper assesses the medium-range forecast process and the application of the NCEP EFS at the South African Weather Service. It includes a description of the various medium-range products, adaptive bias-correction methods applied to the forecasts, verification of the forecast products, and a discussion on the various challenges that face researchers and forecasters alike.

scholarly journals Medium Range Forecast of Maximum Temperature of Coimbatore using ANN

2021 ◽  
Vol 10 (01) ◽  
pp. 3015-3018
Author(s):  
D. Abinayarajam ◽  
S. G. Patil ◽  
Ga. Dheebhakaran ◽  
S. P. Ramanathan
Keyword(s):  
Maximum Temperature ◽  
Medium Range Forecast ◽  
Medium Range

scholarly journals Changes in the Seasonality of Ethiopian Highlands Climate and Implications for Crop Growth

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 892
Author(s):  
Gashaw Bimrew Tarkegn ◽  
Mark R. Jury
Keyword(s):  
Soil Moisture ◽  
Crop Yields ◽  
Regional Scale ◽  
Coupled Model ◽  
Crop Growth ◽  
Potential Evaporation ◽  
Wet Season ◽  
Ethiopian Highlands ◽  
North West ◽  
Long Term Trends

Rain-fed agriculture in North-West (NW) Ethiopia is seasonally modulated, and our objective is to isolate past and future trends that influence crop growth. Statistical methods are applied to gauge-interpolated, reanalysis, and satellite data to evaluate changes in the annual cycle and long-term trends. The June to September wet season has lengthened due to the earlier arrival and later departure of rains. Meteorological composites relate this spreading to local southerly winds and a dry-south/wet-north humidity dipole. At the regional scale, an axis of convection over the Rift Valley (35E) is formed by westerly waves on 15S and an anticyclone over Asia 30N. Coupled Model Intercomparsion Project (CMIP5) Hadley2 data assimilated by the Inter-Sectoral Impact Model Intercomparision Project (ISIMIP) hydrological models are used to evaluate projected soil moisture and potential evaporation over the 21st century. May and October soil moisture is predicted to increase in the future, but trends are weak. In contrast, the potential evaporation is rising and may put stress on the land and water resources. A lengthening of the growing season could benefit crop yields across the NW Ethiopian highlands.

La représentation des surfaces continentales pour la prévision hydrologique

2020 ◽  
pp. 088
Author(s):  
Florence Habets ◽  
Pierre Etchevers ◽  
Patrick Le Moigne
Keyword(s):  
Land Surface ◽  
Preferential Flow ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
Medium Range Forecast ◽  
Sont Encore ◽  
Medium Range ◽  
Forecast Models ◽  
Surface Scheme ◽  
Do So

La modélisation hydrométéorologique initiée par Joël Noilhan permet aujourd'hui d'anticiper les risques de crues sur plusieurs jours, l'évolution de la ressource en eau en France sur plusieurs mois et de projeter les tendances sur le XXIe siècle. Pour cela, il a fallu intégrer des processus sous mailles dans le schéma de surface Isba, car ils sont à l'origine de la genèse d'écoulements préférentiels, et affiner la description de la physiographie. Un des co-bénéfices les plus marquants a été la production d'une réanalyse des variables météorologiques de surface sur la France, aujourd'hui disponible sur plus de 60 ans. Les collaborations initiées avec les hydrologues et acteurs de l'eau se sont encore renforcées, afin de co-construire les modèles de prévisions hydrométéorologiques de demain. The hydrometeorological modeling initiated by Joël Noilhan leads today to short- and medium-range forecast of flood risks, seasonal forecasts of the evolution of the water resource in France and projection of its evolution during the 21st century. To do so, it was necessary to integrate subgrid processes in the land surface scheme ISBA, as they generate preferential flow, and to refine physiographic datasets. One of the most significant co-benef its is the production of a reanalysis of near-surface meteorological variables over France now available over more than 60 years. The initial collaboration with hydrologists and stakeholders has now been strengthened in order to co-design future hydrometeorological forecast models.

Evaluation of Atmospheric Fields from the ECMWF Seasonal Forecasts over a 15-Year Period

2005 ◽  
Vol 18 (16) ◽  
pp. 3250-3269 ◽  
Author(s):  
Geert Jan van Oldenborgh ◽  
Magdalena A. Balmaseda ◽  
Laura Ferranti ◽  
Timothy N. Stockdale ◽  
David L. T. Anderson
Keyword(s):  
Teleconnection Pattern ◽  
Seasonal Forecasts ◽  
Sea Level Pressure ◽  
Weather Forecasts ◽  
Enso Teleconnections ◽  
Medium Range ◽  
Data Points ◽  
System 2 ◽  
System 1 ◽  
Better Than

Abstract Since 1997, the European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification. The main cause of seasonal predictability is El Niño and La Niña perturbing the average weather in many regions and seasons throughout the world. As a baseline to compare the dynamical models with, a set of simple statistical models (STAT) is constructed. These are based on persistence and a lagged regression with the first few EOFs of SST from 1901 to 1986 wherever the correlations are significant. The first EOF corresponds to ENSO, and the second corresponds to decadal ENSO. The temperature model uses one EOF, the sea level pressure (SLP) model uses five EOFs, and the precipitation model uses two EOFs but excludes persistence. As the number of verification data points is very low (15), the simplest measure of skill is used: the correlation coefficient of the ensemble mean. To further reduce the sampling uncertainties, we restrict ourselves to areas and seasons of known ENSO teleconnections. The dynamical ECMWF models show better skill in 2-m temperature forecasts over sea and the tropical land areas than STAT, but the modeled ENSO teleconnection pattern to North America is shifted relative to observations, leading to little pointwise skill. Precipitation forecasts of the ECMWF models are very good, better than those of the statistical model, in southeast Asia, the equatorial Pacific, and the Americas in December–February. In March–May the skill is lower. Overall, S1 (S2) shows better skill than STAT at lead time of 2 months in 29 (32) out of 40 regions and seasons of known ENSO teleconnections.

An Analysis of the Accuracy of 120-h Predictions by the National Meteorological Center's Medium-Range Forecast Model

1994 ◽  
Vol 9 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Mary A. Bedrick ◽  
Anthony J. Cristaldi ◽  
Stephen J. Colucci ◽  
Daniel S. Wilks
Keyword(s):  
Forecast Model ◽  
Medium Range Forecast ◽  
Medium Range

scholarly journals PHOSPHATE AND POTASSIUM FERTILIZATION FOR RADISH GROWN IN A LATOSOL WITH A HIGH CONTENT OF THESE NUTRIENTS

2017 ◽  
Vol 30 (2) ◽  
pp. 412-419 ◽  
Author(s):  
ARTHUR BERNARDES CECÍLIO FILHO ◽  
ALEXSON FILGUEIRAS DUTRA ◽  
GILSON SILVERIO DA SILVA
Keyword(s):  
Ground Water ◽  
Factorial Design ◽  
Leaf Area ◽  
Crop Yields ◽  
Dry Mass ◽  
Plant Leaf ◽  
Potassium Fertilization ◽  
Chemical Fertilization ◽  
Number Of Leaves ◽  
Better Than

ABSTRACT The intensive cultivation of vegetables with frequent chemical fertilization may cause accumulation of nutrients in the soil. This, in turn, may reduce crop yields and damage the environment due to contamination of ground water and rivers. Thus, to increase the effects of P (0, 100, 200, 300 and 400 kg ha -1 of P2O5) and K (0, 60, 120, 180 and 240 kg ha-1 of K2O) doses on the growth and productivity of radish cultivars (Sakata 19 and Sakata 25) in a soil with high levels of these nutrients, two experiments were conducted in randomized blocks with the factors cultivars and doses arranged in a 2 x 5 factorial design with three replications. Number of leaves per plant, leaf area, shoot and root dry mass, total and commercial productivity, percentage of cracked roots and P and K contents in the plant and in the soil were evaluated. The Sakata 19 cultivar performed better than the Sakata 25 in both experiments. The fertilization with P or K did not influence the growth and the productivity of both radish cultivars. Therefore, both cultivars of radish evaluated do not need to be fertilized with P and K when planted in a Latosol with high levels of these nutrients.

scholarly journals Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi

2021 ◽  
Vol 13 (12) ◽  
pp. 2249
Author(s):  
Sadia Alam Shammi ◽  
Qingmin Meng
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Negative Impact ◽  
Positive Impact ◽  
Information Criterion ◽  
Growing Season ◽  
Maximum Temperature ◽  
Linear Regression Models ◽  
The Impact

Climate change and its impact on agriculture are challenging issues regarding food production and food security. Many researchers have been trying to show the direct and indirect impacts of climate change on agriculture using different methods. In this study, we used linear regression models to assess the impact of climate on crop yield spatially and temporally by managing irrigated and non-irrigated crop fields. The climate data used in this study are Tmax (maximum temperature), Tmean (mean temperature), Tmin (minimum temperature), precipitation, and soybean annual yields, at county scale for Mississippi, USA, from 1980 to 2019. We fit a series of linear models that were evaluated based on statistical measurements of adjusted R-square, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). According to the statistical model evaluation, the 1980–1992 model Y[Tmax,Tmin,Precipitation]92i (BIC = 120.2) for irrigated zones and the 1993–2002 model Y[Tmax,Tmean,Precipitation]02ni (BIC = 1128.9) for non-irrigated zones showed the best fit for the 10-year period of climatic impacts on crop yields. These models showed about 2 to 7% significant negative impact of Tmax increase on the crop yield for irrigated and non-irrigated regions. Besides, the models for different agricultural districts also explained the changes of Tmax, Tmean, Tmin, and precipitation in the irrigated (adjusted R-square: 13–28%) and non-irrigated zones (adjusted R-square: 8–73%). About 2–10% negative impact of Tmax was estimated across different agricultural districts, whereas about −2 to +17% impacts of precipitation were observed for different districts. The modeling of 40-year periods of the whole state of Mississippi estimated a negative impact of Tmax (about 2.7 to 8.34%) but a positive impact of Tmean (+8.9%) on crop yield during the crop growing season, for both irrigated and non-irrigated regions. Overall, we assessed that crop yields were negatively affected (about 2–8%) by the increase of Tmax during the growing season, for both irrigated and non-irrigated zones. Both positive and negative impacts on crop yields were observed for the increases of Tmean, Tmin, and precipitation, respectively, for irrigated and non-irrigated zones. This study showed the pattern and extent of Tmax, Tmean, Tmin, and precipitation and their impacts on soybean yield at local and regional scales. The methods and the models proposed in this study could be helpful to quantify the climate change impacts on crop yields by considering irrigation conditions for different regions and periods.

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