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 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 Improvements in medium range weather forecasting system of India

2014 ◽  
Vol 123 (2) ◽  
pp. 247-258 ◽  
Author(s):  
V S PRASAD ◽  
SAJI MOHANDAS ◽  
SURYA KANTI DUTTA ◽  
M DAS GUPTA ◽  
G R IYENGAR ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Medium Range ◽  
Forecasting System

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 Comparative verification in complex topography

2021 ◽  
Author(s):  
Jonas Bhend ◽  
Jean-Christophe Orain ◽  
Vera Schönenberger ◽  
Christoph Spirig ◽  
Lionel Moret ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Absolute Error ◽  
Time Of Day ◽  
Probabilistic Forecasts ◽  
Wide Range ◽  
Direct Model ◽  
Forecasting System ◽  
The Mean ◽  
Update Frequency ◽  
Core Activity

<p>Verification is a core activity in weather forecasting. Insights from verification are used for monitoring, for reporting, to support and motivate development of the forecasting system, and to allow users to maximize forecast value. Due to the broad range of applications for which verification provides valuable input, the range of questions one would like to answer can be very large. Static analyses and summary verification results are often insufficient to cover this broad range. To this end, we developed an interactive verification platform at MeteoSwiss that allows users to inspect verification results from a wide range of angles to find answers to their specific questions.</p><p>We present the technical setup to achieve a flexible yet performant interactive platform and two prototype applications: monitoring of direct model output from operational NWP systems and understanding of the capabilities and limitations of our pre-operational postprocessing. We present two innovations that illustrate the user-oriented approach to comparative verification adopted as part of the platform. To facilitate the comparison of a broad range of forecasts issued with varying update frequency, we rely on the concept of time of verification to collocate the most recent available forecasts at the time of day at which the forecasts are used. In addition, we offer a matrix selection to more flexibly select forecast sources and scores for comparison. Doing so, we can for example compare the mean absolute error (MAE) for deterministic forecasts to the MAE and continuous ranked probability scores of probabilistic forecasts to illustrate the benefit of using probabilistic forecasts.</p>

scholarly journals Temporal and Spatial Interpolation Errors of High-Resolution Modeled Atmospheric Fields

2016 ◽  
Vol 33 (2) ◽  
pp. 303-311 ◽  
Author(s):  
N. C. Privé ◽  
R. M. Errico
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
General Circulation ◽  
Temporal Frequency ◽  
General Circulation Models ◽  
Specific Humidity ◽  
Tropospheric Temperature ◽  
Model Output ◽  
Time Step ◽  
Optimal Output

AbstractGeneral circulation models can now be run at very high spatial resolutions to capture finescale features, but saving the full-spatial-resolution output at every model time step is usually not practical because of storage limitations. To reduce storage requirements, the model output may be produced at reduced temporal and/or spatial resolutions. When this reduced-resolution output is then used in situations where spatiotemporal interpolation is required, such as the generation of synthetic observations for observing system simulation experiments, interpolation errors can significantly affect the quality and usefulness of the reduced-resolution model output. Although it is common in practice to record model output at the highest possible spatial resolution with relatively infrequent temporal output, this may not be the best option to minimize interpolation errors. In this study, two examples using a high-resolution global run of the Goddard Earth Observing System Model, version 5 (GEOS-5), are presented to illustrate cases in which the optimal output dataset configurations for interpolation have high temporal frequency but reduced spatial resolutions. Interpolation errors of tropospheric temperature, specific humidity, and wind fields are investigated. The relationship between spatial and temporal output resolutions and interpolation errors is also characterized for the example model.

Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill

2018 ◽  
Vol 146 (4) ◽  
pp. 1157-1180 ◽  
Author(s):  
Gregory C. Smith ◽  
Jean-Marc Bélanger ◽  
François Roy ◽  
Pierre Pellerin ◽  
Hal Ritchie ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Large Scale ◽  
Weather Forecasting ◽  
Positive Impact ◽  
Weather Prediction ◽  
Ocean Model ◽  
Forecast Skill ◽  
Medium Range ◽  
Forecasting System ◽  
The Impact

The importance of coupling between the atmosphere and the ocean for forecasting on time scales of hours to weeks has been demonstrated for a range of physical processes. Here, the authors evaluate the impact of an interactive air–sea coupling between an operational global deterministic medium-range weather forecasting system and an ice–ocean forecasting system. This system was developed in the context of an experimental forecasting system that is now running operationally at the Canadian Centre for Meteorological and Environmental Prediction. The authors show that the most significant impact is found to be associated with a decreased cyclone intensification, with a reduction in the tropical cyclone false alarm ratio. This results in a 15% decrease in standard deviation errors in geopotential height fields for 120-h forecasts in areas of active cyclone development, with commensurate benefits for wind, temperature, and humidity fields. Whereas impacts on surface fields are found locally in the vicinity of cyclone activity, large-scale improvements in the mid-to-upper troposphere are found with positive global implications for forecast skill. Moreover, coupling is found to produce fairly constant reductions in standard deviation error growth for forecast days 1–7 of about 5% over the northern extratropics in July and August and 15% over the tropics in January and February. To the authors’ knowledge, this is the first time a statistically significant positive impact of coupling has been shown in an operational global medium-range deterministic numerical weather prediction framework.

scholarly journals Downscaling of GCM-Simulated Precipitation Using Model Output Statistics

2014 ◽  
Vol 27 (1) ◽  
pp. 312-324 ◽  
Author(s):  
Jonathan M. Eden ◽  
Martin Widmann
Keyword(s):  
Statistical Downscaling ◽  
Temporal Variability ◽  
General Circulation ◽  
Large Scale ◽  
Principal Component ◽  
General Circulation Models ◽  
Model Output ◽  
Local Scaling ◽  
Simulated Precipitation ◽  
Model Output Statistics

Abstract Producing reliable estimates of changes in precipitation at local and regional scales remains an important challenge in climate science. Statistical downscaling methods are often utilized to bridge the gap between the coarse resolution of general circulation models (GCMs) and the higher resolutions at which information is required by end users. As the skill of GCM precipitation, particularly in simulating temporal variability, is not fully understood, statistical downscaling typically adopts a perfect prognosis (PP) approach in which high-resolution precipitation projections are based on real-world statistical relationships between large-scale atmospheric predictors and local-scale precipitation. Using a nudged simulation of the ECHAM5 GCM, in which the large-scale weather states are forced toward observations of large-scale circulation and temperature for the period 1958–2001, previous work has shown ECHAM5 skill in simulating temporal variability of precipitation to be high in many parts of the world. Here, the same nudged simulation is used in an alternative downscaling approach, based on model output statistics (MOS), in which statistical corrections are derived for simulated precipitation. Cross-validated MOS corrections based on maximum covariance analysis (MCA) and principal component regression (PCR), in addition to a simple local scaling, are shown to perform strongly throughout much of the extratropics. Correlation between downscaled and observed monthly-mean precipitation is as high as 0.8–0.9 in many parts of Europe, North America, and Australia. For these regions, MOS clearly outperforms PP methods that use temperature and circulation as predictors. The strong performance of MOS makes such an approach to downscaling attractive and potentially applicable to climate change simulations.

scholarly journals Dynamic downscaling of the ERA-40 data using a mesoscale meteorological model

2011 ◽  
Vol 12 (1) ◽  
pp. 183 ◽  
Author(s):  
A. PAPADOPOULOS ◽  
G. KORRES ◽  
P. KATSAFADOS ◽  
D. BALLAS ◽  
L. PERIVOLIOTIS ◽  
...  
Keyword(s):  
Dynamical Downscaling ◽  
Weather Forecasting ◽  
Quantitative Estimation ◽  
Three Dimensional ◽  
Atmospheric Model ◽  
Hydrological Models ◽  
Atmospheric Conditions ◽  
Meteorological Model ◽  
Medium Range ◽  
Forecasting System

A sophisticated downscaling procedure that was applied to reproduce high resolution historical records of the atmospheric conditions across the Mediterranean region is presented in this paper. This was accomplished by the dynamical downscaling of the European Center for Medium-Range Forecasts ERA-40 reanalyses with the aid of the atmospheric model of the POSEIDON weather forecasting system. The full three dimensional atmospheric fields with 6 hours of temporal resolution and the surface meteorological parameters at hourly intervals were produced for a 10-year period (1995-2004). The meteorological variables are readily available at 10 km resolution and may constitute the atmospheric forcing to drive wave, ocean hydrodynamic and hydrological models, as well as the baseline data for environmental impact assessment studies. A brief overview of the procedure and a quantitative estimation of the benefit of the new dynamical downscaling dataset are presented.

scholarly journals Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground

2019 ◽  
Vol 12 (7) ◽  
pp. 2797-2809 ◽  
Author(s):  
Sebastian Scher ◽  
Gabriele Messori
Keyword(s):  
Neural Networks ◽  
Seasonal Cycle ◽  
General Circulation ◽  
Climate Model ◽  
Weather Forecasting ◽  
General Circulation Models ◽  
Lead Times ◽  
Climate Forecasting ◽  
Weather And Climate ◽  
The Neural Networks

Abstract. Recently, there has been growing interest in the possibility of using neural networks for both weather forecasting and the generation of climate datasets. We use a bottom–up approach for assessing whether it should, in principle, be possible to do this. We use the relatively simple general circulation models (GCMs) PUMA and PLASIM as a simplified reality on which we train deep neural networks, which we then use for predicting the model weather at lead times of a few days. We specifically assess how the complexity of the climate model affects the neural network's forecast skill and how dependent the skill is on the length of the provided training period. Additionally, we show that using the neural networks to reproduce the climate of general circulation models including a seasonal cycle remains challenging – in contrast to earlier promising results on a model without seasonal cycle.

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