scholarly journals Verification of real-time WRF-ARW forecast in IMD during monsoon 2010

MAUSAM ◽  
2021 ◽  
Vol 67 (2) ◽  
pp. 333-356
Author(s):  
ANANDA K. DAS ◽  
P. K. KUNDU ◽  
S. K. ROY BHOWMIK ◽  
M. RATHEE
Keyword(s):  
Real Time ◽  
Meteorological Parameters ◽  
Mesoscale Model ◽  
Monsoon Season ◽  
Weather Prediction ◽  
India Meteorological Department ◽  
Forecast Errors ◽  
Orographic Rainfall ◽  
Skill Scores ◽  
Model Efficiency

Performance of the mesoscale model WRF-ARW has been evaluated for whole monsoon season of 2011. The real-time model forecasts are generated day to day in India meteorological Department for short-range weather prediction over the Indian region. Verification of rainfall forecasts has been carried out against observed rainfall analysis whereas for all other meteorological parameters verification analysis which was generated using WRFDA assimilation system. Traditional continuous scores and categorical skill scores are computed over seven different zones in India in the verification of rainfall. For other parameters (upper-air as well as surface), continuous scores are evaluated with temporal and spatial features during whole season. The forecast errors of meteorological parameters other than rainfall are analyzed to portray the model efficiency in maintaining monsoon features in large scale along with localized pattern. In the study, time series of errors throughout the season also has been maneuvered to evaluate model forecasts during diverse phases of monsoon. Categorical scores suggest the model forecasts are reliable up to moderate rainfall category for all seven zones.  But, rainfall areas with rainfall above 35.5 mm per day associated with migrated weather system from Indian seas could not be predicted as the model displaces them in the forecast. The verification for a whole monsoon season has shown that the model has capability to predict orographic rainfall for the interactive areas with low level monsoon flow over Western Ghats.  The model efficiency are in general brought out for a single monsoon season and errors characteristics are discussed for further improvement which could not perceived during real-time use of the model. 

scholarly journals Evaluation of heavy rainfall warning over India during summer monsoon season

MAUSAM ◽  
2021 ◽  
Vol 60 (4) ◽  
pp. 475-490
Author(s):  
M. MOHAPATRA ◽  
H. R. HATWAR ◽  
B. K. BANDYOPADHYAY ◽  
V. SUBRAHMANYAM
Keyword(s):  
Summer Monsoon ◽  
Heavy Rainfall ◽  
Monsoon Season ◽  
Weather Prediction ◽  
India Meteorological Department ◽  
Skill Score ◽  
Spatial And Temporal Variability ◽  
Summer Monsoon Season ◽  
Skill Scores ◽  
Heidke Skill Score

India Meteorological Department (IMD) issues heavy rainfall warning for a meteorological sub-division when the expected 24 hours rainfall over any rain gauge station in that sub-division is likely to be 64.5 mm or more. Though these warnings have been provided since the inception of IMD, a few attempts have been made for quantitative evaluation of these warnings.  Hence, a study is undertaken to verify the heavy rainfall warnings over 36 meteorological sub-divisions of India during monsoon months (June-September) and season as a whole. For this purpose, data of recent 5 years (2002-2006) has been taken into consideration. In this connection, the day when heavy rainfall is recorded over at least one station in a sub-division, has been considered as a heavy rainfall day for that sub-division.   There is a large spatial and temporal variability in skill scores of heavy rainfall warnings over India during summer monsoon season. Considering the monsoon season as a whole, the Heidke Skill Score (HSS) is relatively less (<0.20) over the regions with less frequent heavy rainfall like Lakshadweep, southeast peninsula, Vidarbha, Marathwada, Jammu & Kashmir, Arunachal Pradesh and Nagaland, Manipur, Mizoram & Tripura (NMMT). It is higher (> 0.50) over Konkan & Goa, Madhya Maharashtra and Gujarat region. There has been improvement in the forecast skill with gradual increase in the critical success index and Heidke skill score over the years mainly due to the Numerical Weather Prediction (NWP) models' guidance available to the forecasters. However, the false alarm rate and missing rate are still very high (> 0.50), especially over many sub-divisions of northwest India, southeast peninsula and NMMT.

scholarly journals Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts

2008 ◽  
Vol 8 (3) ◽  
pp. 445-460 ◽  
Author(s):  
M. P. Mittermaier
Keyword(s):  
Weather Prediction ◽  
Forecast Skill ◽  
Forecast Errors ◽  
Intensity Scale ◽  
Radar Rainfall ◽  
Scale Method ◽  
Skill Scores ◽  
Forecast Lead Time ◽  
The Uk ◽  
Rainfall Estimates

Abstract. A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP) verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS) and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radar-composite were used. The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.

scholarly journals AROME-WMED, a real-time mesoscale model designed for the HyMeX Special Observation Periods

2015 ◽  
Vol 8 (2) ◽  
pp. 1801-1856 ◽  
Author(s):  
N. Fourrié ◽  
&amp;Eacute;. Bresson ◽  
M. Nuret ◽  
C. Jany ◽  
P. Brousseau ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrological Cycle ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Western Mediterranean ◽  
Western Mediterranean Basin ◽  
Convective Scale ◽  
Observation Instruments ◽  
High Precipitation Events

Abstract. During autumn 2012 and winter 2013, two Special Observation Periods (SOPs) of the Hydrological cycle in the Mediterranean Experiment (HyMeX) took place. For the preparatory studies and to support the instrument deployment during the field campaign, a dedicated version of the operational convective-scale AROME-France model was developed: the AROME-WMED model. It covers the western Mediterranean basin with a 48 h forecast range. It provided real time analyses and forecasts which were sent daily to the HyMeX operational centre to forecast high precipitation events and to help decision makers on the deployment of observation instruments. This paper presents the main features of this numerical weather prediction system in terms of data assimilation and forecast. Some specific data of the HyMeX SOP were assimilated in real time. The forecast skill of the AROME-WMED is then assessed with objective scores and compared to the operational AROME-France model, for both autumn 2012 (5 September to 6 November 2012) and winter 2013 (1 February to 15 March 2013) SOPs. The overall performance of AROME-WMED is good and similar to those of AROME-France for the 0 to 30 h common forecast range. The 24 to 48 h forecast range is of course less accurate but remains useful for scheduling observation deployment. The characteristics of parameters such as precipitation, temperature or humidity, are illustrated by one heavy precipitation case study that occurred over the south of Spain.

scholarly journals The AROME-WMED re-analyses of the first Special Observation Period of the Hydrological cycle in the Mediterranean experiment

2019 ◽  
Author(s):  
Nadia Fourrié ◽  
Mathieu Nuret ◽  
Pierre Brousseau ◽  
Olivier Caumont ◽  
Alexis Doerenbecher ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Error Statistics ◽  
Background Error ◽  
The Real ◽  
Western Mediterranean Basin ◽  
The Mediterranean ◽  
Observation Period

Abstract. To study key processes of the water cycle, two special observation periods (SOPs) of the Hydrological cycle in the Mediterranean experiment (HyMeX) took place during the autumn 2012 and winter 2013. The first SOP aimed to study high precipitation systems and flash-flooding in the Mediterranean area. The AROME-WMED (West-Mediterranean) model (Fourrié et al., 2015) is a dedicated version of the mesoscale Numerical Weather Prediction (NWP) AROME-France model 5 which covers the western Mediterranean basin providing the HyMeX operational centre with daily real-time analyses and forecasts. These products allowed adequate decision-making for the field campaign observation deployment and the instrument operation. Shortly after the end of the campaign, a first re-analysis with more observations was performed with the first SOP operational software. An ensuing comprehensive second re-analysis of the first SOP which included field research observations (not assimilated in real-time), and some reprocessed observation datasets, was made with AROME-WMED. Moreover, a more recent version of the AROME model was used with updated background error statistics for the assimilation process. This paper depicts the main differences between the real-time version and the benefits brought by HyMeX re-analyses with AROME-WMED. The first re-analysis used 9 % of additional data and the second one 24 % more compared to the real-time version. The second re-analysis is found to be closer to observations than the previous AROME-WMED analyses. The second re-analysis forecast errors of surface parameters are reduced up to the 18-h or 24-h forecast range. In the mid and in the upper troposphere, upper-level fields are also improved up to the 48-h forecast range when compared to radiosondes. Integrated Water Vapour comparisons indicate a positive benefit for at least 24 hours. Precipitation forecasts are found to be improved with the second re-analysis for a thresholds up to 10 mm/24-h. For higher thresholds, the frequency bias is degraded. Finally, improvement brought by the second re-analysis is illustrated with the Intensive Observation Period (IOP 8) associated with heavy precipitation over Eastern Spain and South of France.

scholarly journals The AROME-WMED reanalyses of the first special observation period of the Hydrological cycle in the Mediterranean experiment (HyMeX)

2019 ◽  
Vol 12 (7) ◽  
pp. 2657-2678 ◽  
Author(s):  
Nadia Fourrié ◽  
Mathieu Nuret ◽  
Pierre Brousseau ◽  
Olivier Caumont ◽  
Alexis Doerenbecher ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrological Cycle ◽  
Weather Prediction ◽  
Western Mediterranean ◽  
Forecast Errors ◽  
Background Error ◽  
The Real ◽  
Western Mediterranean Basin ◽  
The Mediterranean ◽  
Observation Period

Abstract. To study key processes of the water cycle, two special observation periods (SOPs) of the Hydrological cycle in the Mediterranean experiment (HyMeX) took place during autumn 2012 and winter 2013. The first SOP aimed to study high precipitation systems and flash flooding in the Mediterranean area. The AROME-WMED (western Mediterranean) model (Fourrié et al., 2015) is a dedicated version of the mesoscale Numerical Weather Prediction (NWP) AROME-France model, which covers the western Mediterranean basin providing the HyMeX operational center with daily real-time analyses and forecasts. These products allowed for adequate decision-making for the field campaign observation deployment and the instrument operation. Shortly after the end of the campaign, a first reanalysis with more observations was performed with the first SOP operational software. An ensuing comprehensive second reanalysis of the first SOP, which included field research observations (not assimilated in real time) and some reprocessed observation datasets, was made with AROME-WMED. Moreover, a more recent version of the AROME model was used with updated background error statistics for the assimilation process. This paper depicts the main differences between the real-time version and the benefits brought by HyMeX reanalyses with AROME-WMED. The first reanalysis used 9 % additional data and the second one 24 % more compared to the real-time version. The second reanalysis is found to be closer to observations than the previous AROME-WMED analyses. The second reanalysis forecast errors of surface parameters are reduced up to the 18 and 24 h forecast range. In the middle and upper troposphere, fields are also improved up to the 48 h forecast range when compared to radiosondes. Integrated water vapor comparisons indicate a positive benefit for at least 24 h. Precipitation forecasts are found to be improved with the second reanalysis for a threshold up to 10 mm (24 h)−1. For higher thresholds, the frequency bias is degraded. Finally, improvement brought by the second reanalysis is illustrated with the Intensive Observation Period (IOP8) associated with heavy precipitation over eastern Spain and southern France.

scholarly journals The HMT Multi-Radar Multi-Sensor Hydro Experiment

2017 ◽  
Vol 98 (2) ◽  
pp. 347-359 ◽  
Author(s):  
Steven M. Martinaitis ◽  
Jonathan J. Gourley ◽  
Zachary L. Flamig ◽  
Elizabeth M. Argyle ◽  
Robert A. Clark ◽  
...  
Keyword(s):  
Real Time ◽  
Flash Flood ◽  
Weather Prediction ◽  
The United States ◽  
Test Bed ◽  
Flash Flooding ◽  
Spatial Coverage ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Flood Detection

Abstract There are numerous challenges with the forecasting and detection of flash floods, one of the deadliest weather phenomena in the United States. Statistical metrics of flash flood warnings over recent years depict a generally stagnant warning performance, while regional flash flood guidance utilized in warning operations was shown to have low skill scores. The Hydrometeorological Testbed—Hydrology (HMT-Hydro) experiment was created to allow operational forecasters to assess emerging products and techniques designed to improve the prediction and warning of flash flooding. Scientific goals of the HMT-Hydro experiment included the evaluation of gridded products from the Multi-Radar Multi-Sensor (MRMS) and Flooded Locations and Simulated Hydrographs (FLASH) product suites, including the experimental Coupled Routing and Excess Storage (CREST) model, the application of user-defined probabilistic forecasts in experimental flash flood watches and warnings, and the utility of the Hazard Services software interface with flash flood recommenders in real-time experimental warning operations. The HMT-Hydro experiment ran in collaboration with the Flash Flood and Intense Rainfall (FFaIR) experiment at the Weather Prediction Center to simulate the real-time workflow between a national center and a local forecast office, as well as to facilitate discussions on the challenges of short-term flash flood forecasting. Results from the HMT-Hydro experiment highlighted the utility of MRMS and FLASH products in identifying the spatial coverage and magnitude of flash flooding, while evaluating the perception and reliability of probabilistic forecasts in flash flood watches and warnings. NSSL scientists and NWS forecasters evaluate new tools and techniques through real-time test bed operations for the improvement of flash flood detection and warning operations.

scholarly journals Optical Flow for Verification

2010 ◽  
Vol 25 (5) ◽  
pp. 1479-1494 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe
Keyword(s):  
Optical Flow ◽  
Mesoscale Model ◽  
Forecast Error ◽  
Weather Prediction ◽  
Model Performance ◽  
Simulated Data ◽  
General Idea ◽  
Forecast Errors ◽  
Spatial Fields ◽  
Nwp Model

Abstract Modern numerical weather prediction (NWP) models produce forecasts that are gridded spatial fields. Digital images can also be viewed as gridded spatial fields, and as such, techniques from image analysis can be employed to address the problem of verification of NWP forecasts. One technique for estimating how images change temporally is called optical flow, where it is assumed that temporal changes in images (e.g., in a video) can be represented as a fluid flowing in some manner. Multiple realizations of the general idea have already been employed in verification problems as well as in data assimilation. Here, a specific formulation of optical flow, called Lucas–Kanade, is reviewed and generalized as a tool for estimating three components of forecast error: intensity and two components of displacement, direction and distance. The method is illustrated first on simulated data, and then on a 418-day series of 24-h forecasts of sea level pressure from one member [the Global Forecast System (GFS)–fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)] of the University of Washington’s Mesoscale Ensemble system. The simulation study confirms (and quantifies) the expectation that the method correctly assesses forecast errors. The method is also applied to a real dataset consisting of 418 twenty-four-hour forecasts spanning 2 April 2008–2 November 2009, demonstrating its value for analyzing NWP model performance. Results reveal a significant intensity bias in the subtropics, especially in the southern California region. They also expose a systematic east-northeast or downstream bias of approximately 50 km over land, possibly due to the treatment of terrain in the coarse-resolution model.

scholarly journals Equatorial Waves and the Skill of NCEP and ECMWF Numerical Weather Prediction Systems

2018 ◽  
Vol 146 (6) ◽  
pp. 1763-1784 ◽  
Author(s):  
Juliana Dias ◽  
Maria Gehne ◽  
George N. Kiladis ◽  
Naoko Sakaeda ◽  
Peter Bechtold ◽  
...  
Keyword(s):  
Large Scale ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Forecast Skill ◽  
Lead Times ◽  
Forecast Errors ◽  
Forecast System ◽  
Convective Processes ◽  
Skill Scores ◽  
The Tropics

Despite decades of research on the role of moist convective processes in large-scale tropical dynamics, tropical forecast skill in operational models is still deficient when compared to the extratropics, even at short lead times. Here we compare tropical and Northern Hemisphere (NH) forecast skill for quantitative precipitation forecasts (QPFs) in the NCEP Global Forecast System (GFS) and ECMWF Integrated Forecast System (IFS) during January 2015–March 2016. Results reveal that, in general, initial conditions are reasonably well estimated in both forecast systems, as indicated by relatively good skill scores for the 6–24-h forecasts. However, overall, tropical QPF forecasts in both systems are not considered useful by typical metrics much beyond 4 days. To quantify the relationship between QPF and dynamical skill, space–time spectra and coherence of rainfall and divergence fields are calculated. It is shown that while tropical variability is too weak in both models, the IFS is more skillful in propagating tropical waves for longer lead times. In agreement with past studies demonstrating that extratropical skill is partially drawn from the tropics, a comparison of daily skill in the tropics versus NH suggests that in both models NH forecast skill at lead times beyond day 3 is enhanced by tropical skill in the first couple of days. As shown in previous work, this study indicates that the differences in physics used in each system, in particular, how moist convective processes are coupled to the large-scale flow through these parameterizations, appear as a major source of tropical forecast errors.

India's Ocean Observation Network: Relevance to Society

2016 ◽  
Vol 50 (3) ◽  
pp. 34-46 ◽  
Author(s):  
Ramasamy Venkatesan ◽  
Ranganathan Sundar ◽  
Narayanaswamy Vedachalam ◽  
Karakunnel Jossia Joseph
Keyword(s):  
Real Time ◽  
Weather Prediction ◽  
India Meteorological Department ◽  
Ocean Dynamics ◽  
Time Data ◽  
Sea Level Pressure ◽  
Cyclonic Storm ◽  
Observation Network ◽  
Effective Dissemination ◽  
Observation Systems

AbstractSustained and real-time ocean observation systems using moored data buoys are vital for understanding ocean dynamics and the variability required for improving oceanographic services, including weather prediction, ocean state forecast, and climate change studies. This paper presents the effectiveness of India's moored data buoy network in capturing the response in the ocean during the cyclonic disturbances formed in the Bay of Bengal (BoB) and the Arabian Sea (AS) during the last 5-year period. The moored buoy network provided critical information about the evolution and intensification of low-pressure systems and supported the India Meteorological Department to improve prediction capabilities by incorporating real-time data. The lowest mean sea-level pressure of 920 hPa was recorded in the BoB during the very severe cyclonic storm Phailin. The warning systems adopted for the effective dissemination of the ocean forecast services to the user community have significantly reduced the loss of life and property in coastal states.

scholarly journals AROME-WMED, a real-time mesoscale model designed for the HyMeX special observation periods

2015 ◽  
Vol 8 (7) ◽  
pp. 1919-1941 ◽  
Author(s):  
N. Fourrié ◽  
É. Bresson ◽  
M. Nuret ◽  
C. Jany ◽  
P. Brousseau ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Real Time ◽  
Hydrological Cycle ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Western Mediterranean ◽  
Absolute Difference ◽  
Western Mediterranean Basin ◽  
High Precipitation Events

Abstract. During autumn 2012 and winter 2013, two special observation periods (SOPs) of the HYdrological cycle in the Mediterranean EXperiment (HyMeX) took place. For the preparatory studies and to support the instrument deployment during the field campaign, a dedicated version of the operational convective-scale Application of Research to Operations at Mesoscale (AROME)-France model was developed: the AROME-WMED (West Mediterranean Sea) model. It covers the western Mediterranean basin with a 48 h forecast range. It provided real-time analyses and forecasts which were sent daily to the HyMeX operational centre to forecast high-precipitation events and to help decision makers on the deployment of meteorological instruments. This paper presents the main features of this numerical weather prediction system in terms of data assimilation and forecast. Some specific data of the HyMeX SOP were assimilated in real time. The forecast skill of AROME-WMED is then assessed with objective scores and compared to the operational AROME-France model, for both autumn 2012 (05 September to 06 November 2012) and winter 2013 (01 February to 15 March 2013) SOPs. The overall performance of AROME-WMED is good for the first HyMeX special observation period (SOP1) (i.e. mean 2 m temperature root mean square error (RMSE) of 1.7 °C and mean 2 m relative humidity RMSE of 10 % for the 0–30 h forecast ranges) and similar to those of AROME-France for the 0–30 h common forecast range (maximal absolute difference of 2 m temperature RMSE of 0.2 °C and 0.21 % for the 2 m relative humidity); conversely, for the 24–48 h forecast range it is less accurate (relative loss between 10 and 12 % in 2 m temperature and relative humidity RMSE, and equitable threat score (ETS) for 24 h accumulated rainfall), but it remains useful for scheduling observation deployment. The characteristics of parameters, such as precipitation, temperature or humidity, are illustrated by one heavy precipitation case study that occurred over the south of Spain.

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