scholarly journals Statistical interpretation of general circulation model: A prospect for automation of medium range local weather forecast in India

MAUSAM ◽  
2021 ◽  
Vol 47 (3) ◽  
pp. 229-236
Author(s):  
ASHOK KUMAR ◽  
PARVINDER MAINI
Keyword(s):  
General Circulation ◽  
Weather Forecasting ◽  
Monsoon Season ◽  
Circulation Model ◽  
Weather Forecast ◽  
General Circulation Models ◽  
Statistical Interpretation ◽  
Medium Range Weather Forecast ◽  
Medium Range ◽  
Local Weather

The General Circulation Models (GCM), though able to provide reasonably good medium range weather forecast. have comparatively less skill in forecasting location-specific weather. This is mainly due to the poor representation of 16cal topography and other features in these models. Statistical interpretation (SI) of GCM is very essential in order to improve the location-specific medium range local weather forecast. An attempt has been made at the National Centre for Medium Range Weather Forecasting (NCMRWF), New Delhi to do this type of objective forecasting. Hence location-specific SI models are developed and a bias free forecast is obtained. One of the techniques for accomplishing this, is the Perfect Prog. Method (PPM). PPM models for precipitation (quantitative, probability, yes/no) and maximum minimum temperature are developed for monsoon season (June to August) for 10 stations in lndia. These PPM models and the output from the GCM (R-40) operational at NCMRWF, are then used to obtain the SI forecast. An indirect method based upon SI forecast and observed values of previous one or two seasons, for getting bias free forecast is explained. A comparative study of skill of bias free SI and final forecast, with the observed, issued from NCMRWF to 10 Agromet Field Units (AMFU) during monsoon season 1993, has indicated that automation of medium range local weather forecast can be achieved with the help of SI forecast.

scholarly journals VERIFICATION OF MEDIUM RANGE WEATHER FORECAST IN NORTH GUJARAT

2018 ◽  
Vol 6 (1) ◽  
pp. 102-106
Author(s):  
Sevak Das ◽  
A. I. Desai
Keyword(s):  
Wind Speed ◽  
Minimum Temperature ◽  
Monsoon Season ◽  
Winter Season ◽  
Weather Forecast ◽  
Maximum Temperature ◽  
Medium Range Weather Forecast ◽  
Temperature Forecast ◽  
Lower Accuracy ◽  
Medium Range

The medium range weather forecast issued from NCMRWF, Noida on rainfall, maximum temperature, minimum temperature and wind speed for the last 18 years (1999-2016) has been verified with observed weather parameters recorded at agrometeorological observatory, Sardarkrushinagar to known its accuracy. The results revealed that the usability of rainfall was higher in pre monsoon, post monsoon and winter seasons. However, during monsoon, the accuracy of rainfall forecast was 78 percent with RMSE value of 15.3 that indicated the lower accuracy. The maximum temperature forecast accuracy was very good varied from 76 to 88% in different seasons. Similarly, minimum temperature forecast was excellent in monsoon season (88%), and poor in winter season (57%). The wind speed forecast was excellent in all the seasons.

scholarly journals Evaluation of bias free rainfall forecasts and Kalman filtered temperature forecasts of T-80 model over Indian monsoon region

MAUSAM ◽  
2021 ◽  
Vol 60 (2) ◽  
pp. 147-166
Author(s):  
RASHMI BHARDWAJ ◽  
ASHOK KUMAR ◽  
PARVINDER MAINI
Keyword(s):  
General Circulation ◽  
Weather Forecasting ◽  
General Circulation Models ◽  
Model Output ◽  
Temperature Forecast ◽  
Direct Model ◽  
Surface Weather ◽  
Medium Range ◽  
Forecasting System ◽  
Weather Parameters

  A forecasting system for obtaining objective medium range location specific forecast of surface weather elements is evolved at National Centre for Medium Range Weather Forecasting (NCMRWF). The basic information used for this is the output from   the general circulation models (GCMs) T-80/T-254 operational at NCMRWF. The most essential component of the system is Direct Model Output (DMO) forecast. This is explained in brief.  Direct Model Output (DMO) forecast is obtained from the predicted surface weather elements from the GCM. The two important weather parameters considered in detail are rainfall and temperature. Both the weather parameters  have biases. While the bias from the rainfall is reduced by adopting bias removal technique based upon  threshold values for rainfall and for removing bias from temperature forecast a two parameter Kalman filter is applied. The techniques used for getting bias free forecast are explained in detail. Finally an evaluation of the forecast skill for the  Kalman filtered temperature forecast and  bias free rainfall forecast during monsoon 2007 is presented.

scholarly journals Integration of a relocatable ocean model in the Mediterranean Forecasting System

2006 ◽  
Vol 3 (5) ◽  
pp. 1609-1621 ◽  
Author(s):  
A. Russo ◽  
A. Coluccelli
Keyword(s):  
General Circulation ◽  
Forecast Accuracy ◽  
Weather Forecast ◽  
General Circulation Models ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Medium Range Weather Forecast ◽  
Forecasting System ◽  
The Mediterranean ◽  
Environmental Prediction

Abstract. The MFS (Mediterranean Forecasting System) project and its follower MFSTEP (Mediterranean ocean Forecasting System–Towards Environmental Prediction) are being covering the Mediterranean Sea with operational Ocean General Circulation Models (OGCMs) at horizontal resolution varying from about 12 km till 2005 to 6.5 km in 2006 (reaching 3 km with some regional models and 1.5 km for few shelf models). Heat, water and momentum fluxes through the air-sea interface are derived from the European Center for Medium-range Weather Forecast (ECMWF) output at 0.5° horizontal resolution. Such horizontal resolutions could be not able to provide the needed forecast accuracy in some cases (localized emergencies at sea, e.g. oil spill; need for high resolution current forecasts, e.g. offshore works). A solution to this problem is represented by relocatable models able to be rapidly deployed and to produce forecasts starting from the MFS products. The Harvard Ocean Prediction System (HOPS) has been chosen as base of the relocatable model and it has been interfaced with the MFSTEP OGCM and one regional model. The relocatable model has demonstrated capability to produce forecasts within 2-3 days in many cases, and more rapid implementation may be obtained.

scholarly journals Improving weather forecasting by assimilation of water vapor isotopes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Masataka Tada ◽  
Kei Yoshimura ◽  
Kinya Toride
Keyword(s):  
General Circulation ◽  
Weather Forecasting ◽  
Circulation Model ◽  
General Circulation Models ◽  
Water Isotopes ◽  
Global Dynamics ◽  
Stable Water Isotopes ◽  
The Past ◽  
Using Data ◽  
Made In

AbstractStable water isotopes, which depend on meteorology and terrain, are important indicators of global water circulation. During the past 10 years, major advances have been made in general circulation models that include water isotopes, and the understanding of water isotopes has greatly progressed as a result of innovative, improved observation techniques. However, no previous studies have combined modeled and observed isotopes using data assimilation, nor have they investigated the impacts of real observations of isotopes. This is the first study to assimilate real satellite observations of isotopes using a general circulation model, then investigate the impacts on global dynamics and local phenomena. The results showed that assimilating isotope data improved not only the water isotope field but also meteorological variables such as air temperature and wind speed. Furthermore, the forecast skills of these variables were improved by a few percent, compared with a model that did not assimilate isotope observations.

Inferring precipitation from atmospheric general circulation model variables

2021 ◽  
Author(s):  
Philipp Hess ◽  
Niklas Boers
Keyword(s):  
General Circulation Model ◽  
General Circulation ◽  
Weather Forecasting ◽  
Extreme Values ◽  
Weather Prediction ◽  
Hybrid Approach ◽  
Circulation Model ◽  
Physical Models ◽  
Data Driven ◽  
Medium Range

<p>The accurate prediction of precipitation, in particular of extremes, remains a challenge for numerical weather prediction (NWP) models. A large source of error are subgrid-scale parameterizations of processes that play a crucial role in the complex, multi-scale dynamics of precipitation, but are not explicitly resolved in the model formulation. Recent progress in purely data-driven deep learning for regional precipitation nowcasting [1] and global medium-range forecasting [2] tasks has shown competitive results to traditional NWP models.<br>Here we follow a hybrid approach, in which explicitly resolved atmospheric variables are forecast in time by a general circulation model (GCM) ensemble and then mapped to precipitation using a deep convolutional autoencoder. A frequency-based weighting of the loss function is introduced to improve the learning with regard to extreme values.<br>Our method is validated against a state-of-the-art GCM ensemble using three-hourly high resolution data. The results show an improved representation of extreme precipitation frequencies, as well as comparable error and correlation statistics.<br>   </p><p>[1] C.K. Sønderby et al. "MetNet: A Neural Weather Model for Precipitation Forecasting." arXiv preprint arXiv:2003.12140 (2020). <br>[2] S. Rasp and N. Thuerey "Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution." arXiv preprint arXiv:2008.08626 (2020).</p>

scholarly journals Using Different Formulations of the Transformed Eulerian Mean Equations and Eliassen–Palm Diagnostics in General Circulation Models

2010 ◽  
Vol 67 (6) ◽  
pp. 1983-1995 ◽  
Author(s):  
Steven C. Hardiman ◽  
David G. Andrews ◽  
Andy A. White ◽  
Neal Butchart ◽  
Ian Edmond
Keyword(s):  
General Circulation Model ◽  
General Circulation ◽  
Circulation Model ◽  
General Circulation Models ◽  
Primitive Equations ◽  
Model Output ◽  
Vertical Coordinate ◽  
Order Of Magnitude ◽  
Transformed Eulerian Mean

Abstract Transformed Eulerian mean (TEM) equations and Eliassen–Palm (EP) flux diagnostics are presented for the general nonhydrostatic, fully compressible, deep atmosphere formulation of the primitive equations in spherical geometric coordinates. The TEM equations are applied to a general circulation model (GCM) based on these general primitive equations. It is demonstrated that a naive application in this model of the widely used approximations to the EP diagnostics, valid for the hydrostatic primitive equations using log-pressure as a vertical coordinate and presented, for example, by Andrews et al. in 1987 can lead to misleading features in these diagnostics. These features can be of the same order of magnitude as the diagnostics themselves throughout the winter stratosphere. Similar conclusions are found to hold for “downward control” calculations. The reasons are traced to the change of vertical coordinate from geometric height to log-pressure. Implications for the modeling community, including comparison of model output with that from reanalysis products available only on pressure surfaces, are discussed.

scholarly journals Do Downscaled General Circulation Models Reliably Simulate Historical Climatic Conditions?

2018 ◽  
Vol 22 (10) ◽  
pp. 1-22 ◽  
Author(s):  
Andrew R. Bock ◽  
Lauren E. Hay ◽  
Gregory J. McCabe ◽  
Steven L. Markstrom ◽  
R. Dwight Atkinson
Keyword(s):  
General Circulation ◽  
Statistical Tests ◽  
Circulation Model ◽  
General Circulation Models ◽  
Climatic Conditions ◽  
Low Flow ◽  
Balance Model ◽  
Monthly Precipitation ◽  
Significance Level ◽  
Ks Test

Abstract The accuracy of statistically downscaled (SD) general circulation model (GCM) simulations of monthly surface climate for historical conditions (1950–2005) was assessed for the conterminous United States (CONUS). The SD monthly precipitation (PPT) and temperature (TAVE) from 95 GCMs from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) were used as inputs to a monthly water balance model (MWBM). Distributions of MWBM input (PPT and TAVE) and output [runoff (RUN)] variables derived from gridded station data (GSD) and historical SD climate were compared using the Kolmogorov–Smirnov (KS) test For all three variables considered, the KS test results showed that variables simulated using CMIP5 generally are more reliable than those derived from CMIP3, likely due to improvements in PPT simulations. At most locations across the CONUS, the largest differences between GSD and SD PPT and RUN occurred in the lowest part of the distributions (i.e., low-flow RUN and low-magnitude PPT). Results indicate that for the majority of the CONUS, there are downscaled GCMs that can reliably simulate historical climatic conditions. But, in some geographic locations, none of the SD GCMs replicated historical conditions for two of the three variables (PPT and RUN) based on the KS test, with a significance level of 0.05. In these locations, improved GCM simulations of PPT are needed to more reliably estimate components of the hydrologic cycle. Simple metrics and statistical tests, such as those described here, can provide an initial set of criteria to help simplify GCM selection.

scholarly journals Tracking Scheme Dependence of Simulated Tropical Cyclone Response to Idealized Climate Simulations

2014 ◽  
Vol 27 (24) ◽  
pp. 9197-9213 ◽  
Author(s):  
Michael Horn ◽  
Kevin Walsh ◽  
Ming Zhao ◽  
Suzana J. Camargo ◽  
Enrico Scoccimarro ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
General Circulation ◽  
Climate Model ◽  
Circulation Model ◽  
General Circulation Models ◽  
Tropical Cyclone Activity ◽  
Model Data ◽  
Cyclone Activity ◽  
Sensitivity Testing

Abstract Future tropical cyclone activity is a topic of great scientific and societal interest. In the absence of a climate theory of tropical cyclogenesis, general circulation models are the primary tool available for investigating the issue. However, the identification of tropical cyclones in model data at moderate resolution is complex, and numerous schemes have been developed for their detection. The influence of different tracking schemes on detected tropical cyclone activity and responses in the Hurricane Working Group experiments is examined herein. These are idealized atmospheric general circulation model experiments aimed at determining and distinguishing the effects of increased sea surface temperature and other increased CO2 effects on tropical cyclone activity. Two tracking schemes are applied to these data and the tracks provided by each modeling group are analyzed. The results herein indicate moderate agreement between the different tracking methods, with some models and experiments showing better agreement across schemes than others. When comparing responses between experiments, it is found that much of the disagreement between schemes is due to differences in duration, wind speed, and formation-latitude thresholds. After homogenization in these thresholds, agreement between different tracking methods is improved. However, much disagreement remains, accountable for by more fundamental differences between the tracking schemes. The results indicate that sensitivity testing and selection of objective thresholds are the key factors in obtaining meaningful, reproducible results when tracking tropical cyclones in climate model data at these resolutions, but that more fundamental differences between tracking methods can also have a significant impact on the responses in activity detected.

scholarly journals Ocean Eddy Energetics in the Spectral Space as Revealed by High-Resolution General Circulation Models

2019 ◽  
Vol 49 (11) ◽  
pp. 2815-2827
Author(s):  
Shengpeng Wang ◽  
Zhao Jing ◽  
Qiuying Zhang ◽  
Ping Chang ◽  
Zhaohui Chen ◽  
...  
Keyword(s):  
Energy Conversion ◽  
General Circulation ◽  
Surface Wind ◽  
Circulation Model ◽  
General Circulation Models ◽  
Ocean General Circulation Model ◽  
Energy Cascade ◽  
Spectral Space ◽  
Inverse Energy Cascade ◽  
Barotropic Energy Conversion

AbstractIn this study, the global eddy kinetic energy (EKE) budget in horizontal wavenumber space is analyzed based on 1/10° ocean general circulation model simulations. In both the tropical and midlatitude regions, the barotropic energy conversion from background flow to eddies is positive throughout the wavenumber space and generally peaks at the scale (Le) where EKE reaches its maximum. The baroclinic energy conversion is more pronounced at midlatitudes. It exhibits a dipolar structure with positive and negative values at scales smaller and larger than Le, respectively. Surface wind power on geostrophic flow results in a significant EKE loss around Le but deposits energy at larger scales. The interior viscous dissipation and bottom drag inferred from the pressure flux convergence act as EKE sink terms. The latter is most efficient at Le while the former is more dominant at smaller scales. There is an evident mismatch between EKE generation and dissipation in the spectral space especially at the midlatitudes. This is reconciled by a dominant forward energy cascade on the equator and a dominant inverse energy cascade at the midlatitudes.

scholarly journals Performance Assessment of General Circulation Model in Simulating Daily Precipitation and Temperature Using Multiple Gridded Datasets

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1793 ◽  
Author(s):  
Najeebullah Khan ◽  
Shamsuddin Shahid ◽  
Kamal Ahmed ◽  
Tarmizi Ismail ◽  
Nadeem Nawaz ◽  
...  
Keyword(s):  
General Circulation ◽  
Coupled Model ◽  
Circulation Model ◽  
General Circulation Models ◽  
Daily Maximum ◽  
Gridded Data ◽  
Max Planck ◽  
Meteorological Forcing ◽  
Temperature And Precipitation ◽  
Feature Selection Approach

The performance of general circulation models (GCMs) in a region are generally assessed according to their capability to simulate historical temperature and precipitation of the region. The performance of 31 GCMs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) is evaluated in this study to identify a suitable ensemble for daily maximum, minimum temperature and precipitation for Pakistan using multiple sets of gridded data, namely: Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Berkeley Earth Surface Temperature (BEST), Princeton Global Meteorological Forcing (PGF) and Climate Prediction Centre (CPC) data. An entropy-based robust feature selection approach known as symmetrical uncertainty (SU) is used for the ranking of GCM. It is known from the results of this study that the spatial distribution of best-ranked GCMs varies for different sets of gridded data. The performance of GCMs is also found to vary for both temperatures and precipitation. The Commonwealth Scientific and Industrial Research Organization, Australia (CSIRO)-Mk3-6-0 and Max Planck Institute (MPI)-ESM-LR perform well for temperature while EC-Earth and MIROC5 perform well for precipitation. A trade-off is formulated to select the common GCMs for different climatic variables and gridded data sets, which identify six GCMs, namely: ACCESS1-3, CESM1-BGC, CMCC-CM, HadGEM2-CC, HadGEM2-ES and MIROC5 for the reliable projection of temperature and precipitation of Pakistan.

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