scholarly journals Skill Evaluation of Extended-Range Forecasts of Rainfall and Temperature over the Meteorological Subdivisions of India

2019 ◽  
Vol 34 (1) ◽  
pp. 81-101 ◽  
Author(s):  
Susmitha Joseph ◽  
A. K. Sahai ◽  
R. Phani ◽  
R. Mandal ◽  
A. Dey ◽  
...  
Keyword(s):  
Lead Time ◽  
Coupled Model ◽  
Southwest Monsoon ◽  
Tropical Meteorology ◽  
Ensemble Prediction ◽  
Northeast Monsoon ◽  
Ensemble Prediction System ◽  
Extended Range ◽  
The Government ◽  
Skill Evaluation

Abstract Under the National Monsoon Mission Project initiated by the government of India’s Ministry of Earth Sciences, an indigenous dynamical ensemble prediction system (EPS) has been developed at the Indian Institute of Tropical Meteorology based on the state-of-the-art Climate Forecast System Model version 2 (CFSv2) coupled model, for extended-range (~15–20 days in advance) prediction. The forecasts are generated for the entire year covering the southwest monsoon, the northeast monsoon, and the summer and winter seasons. As the forecast of rainfall is important during the southwest and northeast monsoon seasons, along with that of the temperature during the summer and winter seasons, the present study documents the deterministic as well as probabilistic skill of the EPS in predicting the results in the respective seasons, over various meteorological subdivisions throughout India, on a pentad-lead time scale. The EPS is found to be skillful in predicting rainfall during the southwest and northeast monsoon seasons, as well as temperature during the summer and winter seasons, across different subdivisions of India. In addition, the EPS is noted to be skillful in predicting selected extremes in rainfall and temperature. This affirms the reliability and usefulness of the present EPS from an operational perspective.

scholarly journals Dynamical Extended-Range Prediction of Early Monsoon Rainfall over India

2009 ◽  
Vol 137 (4) ◽  
pp. 1480-1492 ◽  
Author(s):  
Frédéric Vitart ◽  
Franco Molteni
Keyword(s):  
Lead Time ◽  
Monsoon Rainfall ◽  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Variable Resolution ◽  
Sensitivity Experiments ◽  
Extended Range ◽  
Forecasting System

Abstract The 15-member ensembles of 46-day dynamical forecasts starting on each 15 May from 1991 to 2007 have been produced, using the ECMWF Variable Resolution Ensemble Prediction System monthly forecasting system (VarEPS-monthy). The dynamical model simulates a realistic interannual variability of Indian precipitation averaged over the month of June. It also displays some skill to predict Indian precipitation averaged over pentads up to a lead time of about 30 days. This skill exceeds the skill of the ECMWF seasonal forecasting System 3 starting on 1 June. Sensitivity experiments indicate that this is likely due to the higher horizontal resolution of VarEPS-monthly. Another series of sensitivity experiments suggests that the ocean–atmosphere coupling has an important impact on the skill of the monthly forecasting system to predict June rainfall over India.

scholarly journals Projected changes in temperature and precipitation over mainland Southeast Asia by CMIP6 models

2021 ◽  
Author(s):  
S. Supharatid ◽  
J. Nafung ◽  
T. Aribarg
Keyword(s):  
Climate Change ◽  
Coupled Model ◽  
Southwest Monsoon ◽  
Future Climate Change ◽  
Boreal Summer ◽  
Northeast Monsoon ◽  
Adaptation Measures ◽  
Mitigation And Adaptation ◽  
Temperature And Precipitation ◽  
Projected Changes

Abstract Five mainland SEA countries (Cambodia, Laos, Myanmar, Vietnam, and Thailand) are threatened by climate change. Here, the latest 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) is employed to examine future climate change in this region under two SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios (SSP2-4.5 and SSP5-8.5). The bias-corrected multi-model ensemble (MME) projects a warming (wetting) over Cambodia, Laos, Myanmar, Vietnam, and Thailand by 1.88–3.89, 2.04–4.22, 1.88–4.09, 2.03–4.25, and 1.90–3.96 °C (8.76–20.47, 12.69–21.10, 9.54–21.10, 13.47–22.12, and 7.03–15.17%) in the 21st century with larger values found under SSP5-8.5 than SSP2-4.5. The MME model displays approximately triple the current rainfall during the boreal summer. Overall, there are robust increases in rainfall during the Southwest Monsoon (3.41–3.44, 8.44–9.53, and 10.89–17.59%) and the Northeast Monsoon (−2.58 to 0.78, −0.43 to 2.81, and 2.32 to 5.45%). The effectiveness of anticipated climate change mitigation and adaptation strategies under SSP2-4.5 results in slowing down the warming trends and decreasing precipitation trends after 2050. All these findings imply that member countries of mainland SEA need to prepare for appropriate adaptation measures in response to the changing climate.

scholarly journals The skill assessment of ENSO prediction issued by JMA ensemble prediction system and CFSv2

2021 ◽  
Vol 893 (1) ◽  
pp. 012047
Author(s):  
R Rahmat ◽  
A M Setiawan ◽  
Supari
Keyword(s):  
Southern Oscillation ◽  
Skill Score ◽  
Skill Assessment ◽  
Japan Meteorological Agency ◽  
Ensemble Prediction ◽  
In Situ Observation ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Enso Prediction ◽  
The Government

Abstract Indonesian climate is strongly affected by El Niño-Southern Oscillation (ENSO) as one of climate-driven factor. ENSO prediction during the upcoming months or year is crucial for the government in order to design the further strategic policy. Besides producing its own ENSO prediction, BMKG also regularly releases the status and ENSO prediction collected from other climate centers, such as Japan Meteorological Agency (JMA) and National Oceanic and Atmospheric Administration (NOAA). However, the skill of these products is not well known yet. The aim of this study is to conduct a simple assessment on the skill of JMA Ensemble Prediction System (EPS) and NOAA Climate Forecast System version 2 (CFSv2) ENSO prediction using World Meteorological Organization (WMO) Standard Verification System for Long Range Forecast (SVS-LRF) method. Both ENSO prediction results also compared each other using Student's t-test. The ENSO predictions data were obtained from the ENSO JMA and ENSO NCEP forecast archive files, while observed Nino 3.4 were calculated from Centennial in situ Observation-Based Estimates (COBE) Sea Surface Temperature Anomaly (SSTA). Both ENSO prediction issued by JMA and NCEP has a good skill on 1 to 3 months lead time, indicated by high correlation coefficient and positive value of Mean Square Skill Score (MSSS). However, the skill of both skills significantly reduced for May-August target month. Further careful interpretation is needed for ENSO prediction issued on this mentioned period.

Probabilistic precipitation nowcast for flash flooding purposes across Australia: verification of a new version of Short-Term Ensemble Prediction System (STEPS)

2021 ◽  
Author(s):  
Carlos Velasco-Forero ◽  
Jayaram Pudashine ◽  
Mark Curtis ◽  
Alan Seed
Keyword(s):  
Lead Time ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Prediction System ◽  
Short Term ◽  
Radar Rainfall ◽  
Flash Flooding ◽  
Ensemble Prediction System ◽  
Study Results

<div> <p>Short-term precipitation forecast plays a vital role for minimizing the adverse effects of heavy precipitation events such as flash flooding.  Radar rainfall nowcasting techniques based on statistical extrapolations are used to overcome current limitations of precipitation forecasts from numerical weather models, as they provide high spatial and temporal resolutions forecasts within minutes of the observation time. Among various algorithms, the Short-Term Ensemble Prediction System (STEPS) provides rainfall fields nowcasts in a probabilistic sense by accounting the uncertainty in the precipitation forecasts by means of ensembles, with spatial and temporal characteristic very similar to those in the observed radar rainfall fields. The Australian Bureau of Meteorology uses STEPS to generate ensembles of forecast rainfall ensembles in real-time from its extensive weather radar network. </p> </div><div> <p>In this study, results of a large probabilistic verification exercise to a new version of STEPS (hereafter named STEPS-3) are reported. An extensive dataset of more than 47000 individual 5-minute radar rainfall fields (the equivalent of more than 163 days of rain) from ten weather radars across Australia (covering tropical to mid-latitude regions) were used to generate (and verify) 96-member rainfall ensembles nowcasts with up to a 90-minute lead time. STEPS-3 was found to be more than 15-times faster in delivering results compared with previous version of STEPS and an open-source algorithm called pySTEPS. Interestingly, significant variations were observed in the quality of predictions and verification results from one radar to other, from one event to other, depending on the characteristics and location of the radar, nature of the rainfall event, accumulation threshold and lead time. For example, CRPS and RMSE of ensembles of 5-min rainfall forecasts for radars located in mid-latitude regions are better (lower) than those ones from radars located in tropical areas for all lead-times. Also, rainfall fields from S-band radars seem to produce rainfall forecasts able to successfully identify extreme rainfall events for lead times up to 10 minutes longer than those produced using C-band radar datasets for the same rain rate thresholds. Some details of the new STEPS-3 version, case studies and examples of the verification results will be presented. </p> </div>

scholarly journals Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models

Atmosphere ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 458 ◽  
Author(s):  
Zhenzhen Liu ◽  
Qiang Dai ◽  
Lu Zhuo
Keyword(s):  
Lead Time ◽  
Rainfall Variability ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Short Term ◽  
Radar Rainfall ◽  
Ensemble Prediction System ◽  
Predictive Skill ◽  
Many Sources

Radar rainfall nowcasts are subject to many sources of uncertainty and these uncertainties change with the characteristics of a storm. The predictive skill of a radar rainfall nowcasting model can be difficult to understand as sometimes it appears to be perfect but at other times it is highly inaccurate. This hinders the decision making required for the early warning of natural hazards caused by rainfall. In this study we define radar spatial and temporal rainfall variability and relate them to the predictive skill of a nowcasting model. The short-term ensemble prediction system model is configured to predict 731 events with lead times of one, two, and three hours. The nowcasting skill is expressed in terms of six well-known indicators. The results show that the quality of radar rainfall nowcasts increases with the rainfall autocorrelation and decreases with the rainfall variability coefficient. The uncertainty of radar rainfall nowcasts also shows a positive connection with rainfall variability. In addition, the spatial variability is more important than the temporal variability. Based on these results, we recommend that the lead time for radar rainfall nowcasting models should change depending on the storm and that it should be determined according to the rainfall variability. Such measures could improve trust in the rainfall nowcast products that are used for hydrological and meteorological applications.

scholarly journals Prospects for Improving Subseasonal Predictions

2011 ◽  
Vol 139 (11) ◽  
pp. 3648-3666 ◽  
Author(s):  
Kathy Pegion ◽  
Prashant D. Sardeshmukh
Keyword(s):  
Time Lag ◽  
Thermal Inertia ◽  
Coupled Model ◽  
Longwave Radiation ◽  
Forecast Skill ◽  
Ensemble Prediction ◽  
Coupled Models ◽  
Ensemble Prediction System ◽  
Predictability Analysis ◽  
The Tropics

Abstract Extending atmospheric prediction skill beyond the predictability limit of about 10 days for daily weather rests on the hope that some time-averaged aspects of anomalous circulations remain predictable at longer forecast lead times, both because of the existence of natural low-frequency modes of atmospheric variability and coupling to the ocean with larger thermal inertia. In this paper the week-2 and week-3 forecast skill of two global coupled atmosphere–ocean models recently developed at NASA and NOAA is compared with that of much simpler linear inverse models (LIMs) based on the observed time-lag correlations of atmospheric circulation anomalies in the Northern Hemisphere and outgoing longwave radiation (OLR) anomalies in the tropics. The coupled models are found to beat the LIMs only slightly, and only if an ensemble prediction methodology is employed. To assess the potential for further skill improvement, a predictability analysis based on the relative magnitudes of forecast signal and forecast noise in the LIM framework is conducted. Estimating potential skill by such a method is argued to be superior to using the ensemble-mean and ensemble-spread information in the coupled model ensemble prediction system. The LIM-based predictability analysis yields relatively conservative estimates of the potential skill, and suggests that outside the tropics the average coupled model skill may already be close to the potential skill, although there may still be room for improvement in the tropical forecast skill.

Extended Range Forecast of Monsoon at Smaller Spatial Domains over India for Application in Agriculture

2021 ◽  
Author(s):  
D. R. Pattanaik ◽  
Ashish Alone ◽  
Praveen Kumar ◽  
R. Phani ◽  
Raju Mandal ◽  
...  
Keyword(s):  
Lead Time ◽  
Monsoon Season ◽  
Active Phase ◽  
Southwest Monsoon ◽  
Extended Range ◽  
Spatial Domains ◽  
Northwest India ◽  
Correct Forecast ◽  
Subdivision Level ◽  
Onset Of Monsoon

Abstract The performance of operational extended range forecast (ERF) issued by IMD is evaluated for the southwest monsoon 2020. The early onset of monsoon over the Bay of Bengal and the normal onset of monsoon over Kerala with slightly rapid progress northward are very well captured in the ERF with two to three weeks lead time. The ERF also captured very well the transitions from normal to weaker phase of monsoon in July, the active phase of monsoon in entire August was well anticipated in ERF with a lead time of 3 weeks. The active monsoon condition in the second half of September associated with delayed withdrawal from northwest India was also reasonably well captured in the ERF. Quantitatively, the ERF shows significant skill up to three weeks on all India levels. The spatial distribution of met-subdivision level mean forecast skill of predicting above normal, normal and below normal categories in terms of correct (forecast and observed category matching) and partially correct (forecast and observed category out by one category) for the 36 subdivisions during the entire monsoon season of 2020 is found to be 89%, 83%, 80% and 78% for week 1 to week 4 forecasts respectively. The wrong forecasts (forecast and observed category out by two categories) are found to be between 11% in week 1 to 22% in week 4 forecast. Thus, the met-subdivision level forecast shows useful skill and is being used operationally for agrometeorological advisory services of IMD.

scholarly journals Multimodel Ensembling of Subseasonal Precipitation Forecasts over North America

2017 ◽  
Vol 145 (10) ◽  
pp. 3913-3928 ◽  
Author(s):  
N. Vigaud ◽  
A. W. Robertson ◽  
M. K. Tippett
Keyword(s):  
United States ◽  
Lead Time ◽  
Grid Point ◽  
Ensemble Prediction ◽  
Southwestern United States ◽  
Good Reliability ◽  
Ensemble Prediction System ◽  
Regression Parameters ◽  
Skill Scores ◽  
Probabilistic Forecasts

Probabilistic forecasts of weekly and week 3–4 averages of precipitation are constructed using extended logistic regression (ELR) applied to three models (ECMWF, NCEP, and CMA) from the Subseasonal-to-Seasonal (S2S) project. Individual and multimodel ensemble (MME) forecasts are verified over the common period 1999–2010. The regression parameters are fitted separately at each grid point and lead time for the three ensemble prediction system (EPS) reforecasts with starts during January–March and July–September. The ELR produces tercile category probabilities for each model that are then averaged with equal weighting. The resulting MME forecasts are characterized by good reliability but low sharpness. A clear benefit of multimodel ensembling is to largely remove negative skill scores present in individual forecasts. The forecast skill of weekly averages is higher in winter than summer and decreases with lead time, with steep decreases after one and two weeks. Week 3–4 forecasts have more skill along the U.S. East Coast and the southwestern United States in winter, as well as over west/central U.S. regions and the intra-American sea/east Pacific during summer. Skill is also enhanced when the regression parameters are fit using spatially smoothed observations and forecasts. The skill of week 3–4 precipitation outlooks has a modest, but statistically significant, relation with ENSO and the MJO, particularly in winter over the southwestern United States.

scholarly journals Predictability and Prediction Skill of the MJO in Two Operational Forecasting Systems

2014 ◽  
Vol 27 (14) ◽  
pp. 5364-5378 ◽  
Author(s):  
Hye-Mi Kim ◽  
Peter J. Webster ◽  
Violeta E. Toma ◽  
Daehyun Kim
Keyword(s):  
Growth Rate ◽  
Lead Time ◽  
Propagation Speed ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Climate Forecast System ◽  
Ensemble Prediction System ◽  
Variable Resolution ◽  
Bivariate Correlation ◽  
Operational Forecasting

Abstract The authors examine the predictability and prediction skill of the Madden–Julian oscillation (MJO) of two ocean–atmosphere coupled forecast systems of ECMWF [Variable Resolution Ensemble Prediction System (VarEPS)] and NCEP [Climate Forecast System, version 2 (CFSv2)]. The VarEPS hindcasts possess five ensemble members for the period 1993–2009 and the CFSv2 hindcasts possess three ensemble members for the period 2000–09. Predictability and prediction skill are estimated by the bivariate correlation coefficient between the observed and predicted Wheeler–Hendon real-time multivariate MJO index (RMM). MJO predictability is beyond 32 days lead time in both hindcasts, while the prediction skill is about 27 days in VarEPS and 21 days in CFSv2 as measured by the bivariate correlation exceeding 0.5. Both predictability and prediction skill of MJO are enhanced by averaging ensembles. Results show clearly that forecasts initialized with (or targeting) strong MJOs possess greater prediction skill compared to those initialized with (or targeting) weak or nonexistent MJOs. The predictability is insensitive to the initial MJO phase (or forecast target phase), although the prediction skill varies with MJO phases. A few common model issues are identified. In both hindcasts, the MJO propagation speed is slower and the MJO amplitude is weaker than observed. Also, both ensemble forecast systems are underdispersive, meaning that the growth rate of ensemble error is greater than the growth rate of the ensemble spread by lead time.

Sign in / Sign up

Export Citation Format

Share Document