scholarly journals Utility of Global Ensemble Forecast System (GEFS) Reforecast for Medium-Range Drought Prediction in India

2016 ◽  
Vol 17 (6) ◽  
pp. 1781-1800 ◽  
Author(s):  
Reepal D. Shah ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Monsoon Season ◽  
Ensemble Forecast ◽  
Extreme Weather Events ◽  
Forecast System ◽  
Drought Prediction ◽  
Air Temperatures ◽  
Medium Range ◽  
Precipitation And Temperature

Abstract Medium-range (~7 days) forecasts of agricultural and hydrologic droughts can help in decision-making in agriculture and water resources management. India has witnessed severe losses due to extreme weather events during recent years and medium-range forecasts of precipitation, air temperatures (maximum and minimum), and hydrologic variables (root-zone soil moisture and runoff) can be valuable. Here, the skill of the Global Ensemble Forecast System (GEFS) reforecast of precipitation and air temperatures is evaluated using retrospective data for the period of 1985–2010. It is found that the GEFS forecast shows better skill in the nonmonsoon season than in the monsoon season in India. Moreover, skill in temperature forecast is higher than that of precipitation in both the monsoon and nonmonsoon seasons. The lower skill in forecasting precipitation during the monsoon season can be attributed to representation of intraseasonal variability in precipitation from the GEFS. Among the selected regions, the northern, northeastern, and core monsoon region showed relatively lower skill in the GEFS forecast. Temperature and precipitation forecasts were corrected from the GEFS using quantile–quantile (Q–Q) mapping and linear scaling, respectively. Bias-corrected forecasts for precipitation and air temperatures were improved over the raw forecasts. The influence of corrected and raw forcings on medium-range soil moisture, drought, and runoff forecasts was evaluated. The results showed that because of high persistence, medium-range soil moisture forecasts are largely determined by the initial hydrologic conditions. Bias correction of precipitation and temperature forecasts does not lead to significant improvement in the medium-range hydrologic forecasting of soil moisture and drought. However, bias correcting raw GEFS forecasts can provide better predictions of the forecasts of precipitation and temperature anomalies over India.

scholarly journals Short-to-medium range hydrologic forecast to manage water and agricultural resources in India

2016 ◽  
Author(s):  
Reepal Shah ◽  
Atul Kumar Sahai ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Root Zone ◽  
Tropical Meteorology ◽  
Forecast System ◽  
Temperature Forecast ◽  
Ensemble Mean ◽  
Medium Range ◽  
Prediction Systems ◽  
Hydrologic Forecast

Abstract. Water resources and agriculture are often affected by the weather anomalies in India resulting in a disproportionate damage. While short to medium range prediction systems and forecast products are available, a skilful hydrologic forecast of runoff and root-zone soil moisture that can provide timely information has been lacking in India. Using precipitation and air temperature forecasts from the Climate Forecast System v2 (CFSv2), Global Ensemble Forecast System (GEFSv2) and four products from Indian Institute of Tropical Meteorology (IITM), here we show that the IITM ensemble mean (mean of all four products from IITM) can be used operationally to provide hydrologic forecast in India at 7–45 days lead time. The IITM ensemble mean forecast was further improved using bias correction for precipitation and air temperature. Forecast based on the IITM-ensemble mean showed better skill in majority of India for all the lead times (7–45 days) in comparison to the other forecast products. Moreover, the VIC simulated forecast of runoff and soil moisture successfully captured the observed anomalies during the severe droughts years. The findings reported herein have strong implications for providing timely information that can help farmers and water managers in decision making in India.

scholarly journals Anthropogenic warming and intraseasonal summer monsoon variability amplify the risk of future flash droughts in India

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Vimal Mishra ◽  
Saran Aadhar ◽  
Shanti Shwarup Mahto
Keyword(s):  
Soil Moisture ◽  
Summer Monsoon ◽  
Crop Production ◽  
Monsoon Rainfall ◽  
Root Zone ◽  
Monsoon Season ◽  
Intraseasonal Variability ◽  
Positive Temperature ◽  
Groundwater Abstraction ◽  
Increased Risk

AbstractFlash droughts cause rapid depletion in root-zone soil moisture and severely affect crop health and irrigation water demands. However, their occurrence and impacts in the current and future climate in India remain unknown. Here we use observations and model simulations from the large ensemble of Community Earth System Model to quantify the risk of flash droughts in India. Root-zone soil moisture simulations conducted using Variable Infiltration Capacity model show that flash droughts predominantly occur during the summer monsoon season (June–September) and driven by the intraseasonal variability of monsoon rainfall. Positive temperature anomalies during the monsoon break rapidly deplete soil moisture, which is further exacerbated by the land-atmospheric feedback. The worst flash drought in the observed (1951–2016) climate occurred in 1979, affecting more than 40% of the country. The frequency of concurrent hot and dry extremes is projected to rise by about five-fold, causing approximately seven-fold increase in flash droughts like 1979 by the end of the 21st century. The increased risk of flash droughts in the future is attributed to intraseasonal variability of the summer monsoon rainfall and anthropogenic warming, which can have deleterious implications for crop production, irrigation demands, and groundwater abstraction in India.

scholarly journals Medium-Range Convection-Allowing Ensemble Forecasts with a Variable-Resolution Global Model

2019 ◽  
Vol 147 (8) ◽  
pp. 2997-3023 ◽  
Author(s):  
Craig S. Schwartz
Keyword(s):  
High Resolution ◽  
Ensemble Forecast ◽  
The Other ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Variable Resolution ◽  
Cell Spacing ◽  
Medium Range ◽  
Potential Benefits ◽  
Convective Mode

Abstract Two sets of global, 132-h (5.5-day), 10-member ensemble forecasts were produced with the Model for Prediction Across Scales (MPAS) for 35 cases in April and May 2017. One MPAS ensemble had a quasi-uniform 15-km mesh while the other employed a variable-resolution mesh with 3-km cell spacing over the conterminous United States (CONUS) that smoothly relaxed to 15 km over the rest of the globe. Precipitation forecasts from both MPAS ensembles were objectively verified over the central and eastern CONUS to assess the potential benefits of configuring MPAS with a 3-km mesh refinement region for medium-range forecasts. In addition, forecasts from NCEP’s operational Global Ensemble Forecast System were evaluated and served as a baseline against which to compare the experimental MPAS ensembles. The 3-km MPAS ensemble most faithfully reproduced the observed diurnal cycle of precipitation throughout the 132-h forecasts and had superior precipitation skill and reliability over the first 48 h. However, after 48 h, the three ensembles had more similar spread, reliability, and skill, and differences between probabilistic precipitation forecasts derived from the 3- and 15-km MPAS ensembles were typically statistically insignificant. Nonetheless, despite fewer benefits of increased resolution for spatial placement after 48 h, 3-km ensemble members explicitly provided potentially valuable guidance regarding convective mode throughout the 132-h forecasts while the other ensembles did not. Collectively, these results suggest both strengths and limitations of medium-range high-resolution ensemble forecasts and reveal pathways for future investigations to improve understanding of high-resolution global ensembles with variable-resolution meshes.

scholarly journals Land Surface Parameter and State Perturbations in the Global Ensemble Forecast System

2019 ◽  
Vol 147 (4) ◽  
pp. 1319-1340
Author(s):  
Maria Gehne ◽  
Thomas M. Hamill ◽  
Gary T. Bates ◽  
Philip Pegion ◽  
Walter Kolczynski
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Systematic Bias ◽  
Perturbation Scheme ◽  
Forecast System ◽  
Vegetation Fraction ◽  
Land Surface Parameter

Abstract The National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) is underdispersive near the surface, a common characteristic of ensemble prediction systems. Here, several methods for increasing the spread are tested, including perturbing soil initial conditions, soil tendencies, and surface parameters, with physically based perturbations. Perturbations are applied to the soil initial conditions based on empirical orthogonal functions (EOFs) of differences between normalized soil moisture states from two land surface models (LSMs). Perturbations to roughness lengths for heat and momentum, soil hydraulic conductivity, stomatal resistance, vegetation fraction, and albedo are applied, with the amplitude and perturbation scales based on previous research. Soil moisture and temperature tendencies are also perturbed using a stochastic perturbation scheme. The results show that surface perturbations, through their impact on 2-m temperature spread, have a modest positive impact on the skill of short-range ensemble forecasts. However, adjusting the forecasts using an estimate of the systematic bias shows that bias correction has a greater impact on the forecast reliability than surface perturbations, indicating that systematic bias in the model needs to be addressed as well.

scholarly journals Evaluation of Probabilistic Medium-Range Temperature Forecasts from the North American Ensemble Forecast System

2009 ◽  
Vol 24 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Doug McCollor ◽  
Roland Stull
Keyword(s):  
North American ◽  
Skill Score ◽  
Ensemble Forecast ◽  
Value Assessment ◽  
Operating Characteristics ◽  
Forecast System ◽  
Forecast Period ◽  
The North ◽  
Medium Range ◽  
The Future

Abstract Ensemble temperature forecasts from the North American Ensemble Forecast System were assessed for quality against observations for 10 cities in western North America, for a 7-month period beginning in February 2007. Medium-range probabilistic temperature forecasts can provide information for those economic sectors exposed to temperature-related business risk, such as agriculture, energy, transportation, and retail sales. The raw ensemble forecasts were postprocessed, incorporating a 14-day moving-average forecast–observation difference, for each ensemble member. This postprocessing reduced the mean error in the sample to 0.6°C or less. It is important to note that the North American Ensemble Forecast System available to the public provides bias-corrected maximum and minimum temperature forecasts. Root-mean-square-error and Pearson correlation skill scores, applied to the ensemble average forecast, indicate positive, but diminishing, forecast skill (compared to climatology) from 1 to 9 days into the future. The probabilistic forecasts were evaluated using the continuous ranked probability skill score, the relative operating characteristics skill score, and a value assessment incorporating cost–loss determination. The full suite of ensemble members provided skillful forecasts 10–12 days into the future. A rank histogram analysis was performed to test ensemble spread relative to the observations. Forecasts are underdispersive early in the forecast period, for forecast days 1 and 2. Dispersion improves rapidly but remains somewhat underdispersive through forecast day 6. The forecasts show little or no dispersion beyond forecast day 6. A new skill versus spread diagram is presented that shows the trade-off between higher skill but low spread early in the forecast period and lower skill but better spread later in the forecast period.

scholarly journals Drought Indices Based on the Climate Forecast System Reanalysis and Ensemble NLDAS

2011 ◽  
Vol 12 (2) ◽  
pp. 181-205 ◽  
Author(s):  
Kingtse C. Mo ◽  
Lindsey N. Long ◽  
Youlong Xia ◽  
S. K. Yang ◽  
Jae E. Schemm ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
North American ◽  
Root Zone ◽  
The United States ◽  
Drought Indices ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis

Abstract Drought indices derived from the Climate Forecast System Reanalysis (CFSR) are compared with indices derived from the ensemble North American Land Data Assimilation System (NLDAS) and the North American Regional Reanalysis (NARR) over the United States. Uncertainties in soil moisture, runoff, and evapotranspiration (E) from three systems are assessed by comparing them with limited observations, including E from the AmeriFlux data, soil moisture from the Oklahoma Mesonet and the Illinois State Water Survey, and streamflow data from the U.S. Geological Survey (USGS). The CFSR has positive precipitation (P) biases over the western mountains, the Pacific Northwest, and the Ohio River valley in winter and spring. In summer, it has positive biases over the Southeast and large negative biases over the Great Plains. These errors limit the ability to use the standardized precipitation indices (SPIs) derived from the CFSR to measure the severity of meteorological droughts. To compare with the P analyses, the Heidke score for the 6-month SPI derived from the CFSR is on average about 0.5 for the three-category classification of drought, floods, and neutral months. The CFSR has positive E biases in spring because of positive biases in downward solar radiation and high potential evaporation. The negative E biases over the Great Plains in summer are due to less P and soil moisture in the root zone. The correlations of soil moisture percentile between the CFSR and the ensemble NLDAS are regionally dependent. The correlations are higher over the area east of 100°W and the West Coast. There is less agreement between them over the western interior region.

scholarly journals A novel way of correcting between-ensemble-member biases in a lagged S2S ensemble

2021 ◽  
Author(s):  
Marion Mittermaier ◽  
Seshagiri Rao Kolusu ◽  
Joanne Robbins
Keyword(s):  
Lead Time ◽  
Monsoon Season ◽  
Seasonal Forecast ◽  
Ensemble Forecast ◽  
Ensemble Member ◽  
Evaluation Methodology ◽  
Seasonal Forecasts ◽  
Forecast System ◽  
Forecast Horizon ◽  
The Impact

<p>The UK Met Office seasonal forecast system, Global Seasonal Forecast System version 5 (GloSea5), is an ensemble forecast prediction system providing sub-seasonal and seasonal forecasts over the globe with ~60 km resolution in the mid-latitudes. GloSea5 also produces hindcasts or historical re-forecasts. The system produces 4 members a day, initialised at 00UTC. Two members run out to 64 days and two run out to 216 days. We use these four members to generate a 40-member lagged ensemble with 10 days of lag time, i.e. for any forecast horizon the oldest members are always 10 days older. Due to this lag and the way these ensemble members are initialised, there is a considerable within-ensemble bias, even for a nominal “day 1” forecast. This within-ensemble bias evolves with increasing lead time horizon.</p><p>Traditionally hindcasts are used to correct for the so-called model drift. In this work the idea of using a distribution of daily rainfall amounts from short-lead time forecasts is used using the 2019 Indian monsoon season. Quantile mapping is trialled as a means of removing the “within-ensemble-member” bias to ensure that all ensemble members are drawn from a more consistent underlying distribution. Achieving this would enable the members to be used to drive downstream applications such as hazard or impact models, as such models require individual ensemble members.</p><p>The presentation will demonstrate the methodology and the impact it has on ensemble forecast skill, complementing the presentation by Kolusu et al. (same session in conference) which presents an evaluation methodology focusing on patterns for different accumulation lengths and forecast horizons.</p>

scholarly journals Performance of the New NCEP Global Ensemble Forecast System in a Parallel Experiment

2017 ◽  
Vol 32 (5) ◽  
pp. 1989-2004 ◽  
Author(s):  
Xiaqiong Zhou ◽  
Yuejian Zhu ◽  
Dingchen Hou ◽  
Yan Luo ◽  
Jiayi Peng ◽  
...  
Keyword(s):  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Forecast Model ◽  
Horizontal Resolution ◽  
Ensemble Forecast ◽  
Wind Fields ◽  
Forecast System ◽  
Medium Range Forecast ◽  
Verification Period ◽  
Medium Range

Abstract A new version of the Global Ensemble Forecast System (GEFS, v11) is tested and compared with the operational version (v10) in a 2-yr parallel run. The breeding-based scheme with ensemble transformation and rescaling (ETR) used in the operational GEFS is replaced by the ensemble Kalman filter (EnKF) to generate initial ensemble perturbations. The global medium-range forecast model and the Global Forecast System (GFS) analysis used as the initial conditions are upgraded to the GFS 2015 implementation version. The horizontal resolution of GEFS increases from Eulerian T254 (~52 km) for the first 8 days of the forecast and T190 (~70 km) for the second 8 days to semi-Lagrangian T574 (~34 km) and T382 (~52 km), respectively. The sigma pressure hybrid vertical layers increase from 42 to 64 levels. The verification of geopotential height, temperature, and wind fields at selected levels shows that the new GEFS significantly outperforms the operational GEFS up to days 8–10 except for an increased warm bias over land in the extratropics. It is also found that the parallel system has better reliability in the short-range probability forecasts of precipitation during warm seasons, but no clear improvement in cold seasons. There is a significant degradation of TC track forecasts at days 6–7 during the 2012–14 TC seasons over the Atlantic and eastern Pacific. This degradation is most likely a sampling issue from a low number of TCs during these three TC seasons. The results for an extended verification period (2011–14) and the recent two hurricane seasons (2015 and 2016) are generally positive. The new GEFS became operational at NCEP on 2 December 2015.

scholarly journals Short to sub-seasonal hydrologic forecast to manage water and agricultural resources in India

2017 ◽  
Vol 21 (2) ◽  
pp. 707-720 ◽  
Author(s):  
Reepal Shah ◽  
Atul Kumar Sahai ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Root Zone ◽  
Tropical Meteorology ◽  
Precipitation Forecast ◽  
Severe Drought ◽  
Forecast System ◽  
Infiltration Capacity ◽  
Ensemble Mean ◽  
Hydrologic Forecast

Abstract. Water resources and agriculture are often affected by the weather anomalies in India resulting in disproportionate damage. While short to sub-seasonal prediction systems and forecast products are available, a skilful hydrologic forecast of runoff and root-zone soil moisture that can provide timely information has been lacking in India. Using precipitation and air temperature forecasts from the Climate Forecast System v2 (CFSv2), the Global Ensemble Forecast System (GEFSv2) and four products from the Indian Institute of Tropical Meteorology (IITM), here we show that the IITM ensemble mean (mean of all four products from the IITM) can be used operationally to provide a hydrologic forecast in India at a 7–45-day accumulation period. The IITM ensemble mean forecast was further improved using bias correction for precipitation and air temperature. Bias corrected precipitation forecast showed an improvement of 2.1 mm (on the all-India median mean absolute error – MAE), while all-India median bias corrected temperature forecast was improved by 2.1 °C for a 45-day accumulation period. Moreover, the Variable Infiltration Capacity (VIC) model simulated forecast of runoff and soil moisture successfully captured the observed anomalies during the severe drought years. The findings reported herein have strong implications for providing timely information that can help farmers and water managers in decision making in India.

ВОДНЫЙ РЕЖИМ ОРОШАЕМЫХ СОЛОНЦОВЫХ КОМПЛЕКСНЫХ ПОЧВ И ОСОБЕННОСТИ ЕГО РЕГУЛИРОВАНИЯ

2020 ◽  
Author(s):  
Valery Yashin
Keyword(s):  
Soil Moisture ◽  
Ground Water ◽  
Irrigation Water ◽  
Surface Runoff ◽  
Root Zone ◽  
Irrigation Methods ◽  
Salt Flats

Представлены материалы исследований формирования режима влажности и динамики грунтовых вод орошаемых солонцовых комплексных почв при различных способах полива, проведенные в Волгоградском Заволжье. Установлена значительная неравномерность распределения влажности почвы при поливах дождеванием. Отмечается поверхностный сток по микрорельефу до 30% от поливной нормы, что приводит к недостаточности увлажнения корневой зоны на солонцах и переувлажнению почв в понижениях микрорельефа и потере оросительной воды на инфильтрационное питание грунтовых вод.The article presents the materials of research on the formation of the humidity regime and dynamics of ground water of irrigated saline complex soils under various irrigation methods, conducted in the Volgograd Zavolzhye. A significant unevenness in the distribution of soil moisture during irrigation with sprinkling has been established. There is a surface runoff on the microrelief of up to 30% of the irrigation norm, which leads to insufficient moisture of the root zone on the salt flats and waterlogging of the soil in the microrelief depressions and loss of irrigation water for infiltration feed of ground water.

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