range forecasts
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MAUSAM ◽  
2021 ◽  
Vol 63 (1) ◽  
pp. 137-148
Author(s):  
P.N. MAHAJAN ◽  
R.M. KHALADKAR ◽  
S.G. NARKHEDKAR ◽  
SATHY NAIR ◽  
AMITA PRABHU ◽  
...  

In this paper, utility of satellite derived atmospheric motion vectors and geophysical parameters is brought out to discern appropriate signals for improving short-range forecasts in respect of development/dissipation of tropical cyclones over the Indian region. Results of a particular case study of May, 2001 cyclone, which formed in the Arabian Sea are reported. Analysis of wind field with input of modified cloud motion vectors and water vapour wind vectors is performed utilizing Optimum Interpolation (OI) technique at 850 and 200 hPa for finding dynamical changes such as vorticity, convergence and divergence for the complete life period of this cyclone. Simultaneously, variations in geophysical parameters obtained from IRS-P4 and TRMM satellites in ascending and descending nodes are compared with dynamical variations for discerning some positive signals to improve short range forecasts over the Indian region. The enhancement of cyclonic vorticity at 200 hPa over larger area surrounding center of cyclone was observed from 26 to 28 May 2001 which gave a positive signal for dissipation of storm.


MAUSAM ◽  
2021 ◽  
Vol 66 (3) ◽  
pp. 375-386
Author(s):  
RAGHAVENDRA ASHRIT ◽  
KULDEEP SHARMA ◽  
ANUMEHA DUBE ◽  
GOPAL IYENGAR ◽  
ASHIS MITRA ◽  
...  

MAUSAM ◽  
2021 ◽  
Vol 50 (2) ◽  
pp. 145-152
Author(s):  
R. M. RAJEEVAN ◽  
V. THAPLIYAL ◽  
S. R. PATIL ◽  
U. S. DE

Using the canonical correlation analysis (CCA) approach, a forecast model for long range forecasts of monsoon (June-September) rainfall of 27 meteorological sub-divisions over India was developed, A set of 12 parameters, which have significant correlation with Indian monsoon rainfall, was used as predictors, The model was developed with the data of the period 1958-1994 and by retaining three significant canonical modes, The model showed useful predictive skill in of respect of meteorological sub-divisions over central parts of India and NW India with low errors and high skill scores for categorical forecasts, The model showed no predictive skill in respect of meteorological sub-division over south peninsula, Orissa, West Bengal and Bihar. The CCA model has been also found to perform better than another statistical model developed using the 12 same predictors, The CCA model also showed moderate skill in forecasting excess and deficient rainfall categories of sub-divisional monsoon rainfall during the extreme years.


2021 ◽  
Vol 11 (22) ◽  
pp. 10852
Author(s):  
Gregor Skok ◽  
Doruntina Hoxha ◽  
Žiga Zaplotnik

This study investigates the potential of direct prediction of daily extremes of temperature at 2 m from a vertical profile measurement using neural networks (NNs). The analysis is based on 3800 daily profiles measured in the period 2004–2019. Various setups of dense sequential NNs are trained to predict the daily extremes at different lead times ranging from 0 to 500 days into the future. The short- to medium-range forecasts rely mainly on the profile data from the lowest layer—mostly on the temperature in the lowest 1 km. For the long-range forecasts (e.g., 100 days), the NN relies on the data from the whole troposphere. The error increases with forecast lead time, but at the same time, it exhibits periodic behavior for long lead times. The NN forecast beats the persistence forecast but becomes worse than the climatological forecast on day two or three. The forecast slightly improves when the previous-day measurements of temperature extremes are added as a predictor. The best forecast is obtained when the climatological value is added as well, with the biggest improvement in the long-term range where the error is constrained to the climatological forecast error.


2021 ◽  
Author(s):  
Adam A. Scaife ◽  
Mark P. Baldwin ◽  
Amy H. Butler ◽  
Andrew J. Charlton-Perez ◽  
Daniella I. V. Domeisen ◽  
...  

Abstract. Over recent years there have been parallel advances in the development of stratosphere resolving numerical models, our understanding of stratosphere-troposphere interaction and the extension of long-range forecasts to explicitly include the stratosphere. These advances are now allowing new and improved capability in long range prediction. We present an overview of this development and show how the inclusion of the stratosphere in forecast systems aids monthly, seasonal and decadal climate predictions. We end with an outlook towards the future of climate forecasts and identify areas for improvement that could further benefit these rapidly evolving predictions.


Fire ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 55
Author(s):  
Gary L. Achtemeier ◽  
Scott L. Goodrick

Abrupt changes in wind direction and speed caused by thunderstorm-generated gust fronts can, within a few seconds, transform slow-spreading low-intensity flanking fires into high-intensity head fires. Flame heights and spread rates can more than double. Fire mitigation strategies are challenged and the safety of fire crews is put at risk. We propose a class of numerical weather prediction models that incorporate real-time radar data and which can provide fire response units with images of accurate very short-range forecasts of gust front locations and intensities. Real-time weather radar data are coupled with a wind model that simulates density currents over complex terrain. Then two convective systems from formation and merger to gust front arrival at the location of a wildfire at Yarnell, Arizona, in 2013 are simulated. We present images of maps showing the progress of the gust fronts toward the fire. Such images can be transmitted to fire crews to assist decision-making. We conclude, therefore, that very short-range gust front prediction models that incorporate real-time radar data show promise as a means of predicting the critical weather information on gust front propagation for fire operations, and that such tools warrant further study.


Author(s):  
R. Yu. Fadeev ◽  
◽  
V.V. Shashkin . ◽  
M.A. Tolstykh ◽  
S.V. Travova ◽  
...  

A brief description is given for the works carried out in 2020 to implement the longrange forecast technology based on the SLAV072L96 multiscale hydrodynamic atmosphere model. The purpose of these works was an improvement in simulating the deep convection and stratosphere dynamics. The works comprised the improvement and verification of the parameterizations for subgrid-scale processes and the whole model using long-range forecasts computed from historical initial data. As a result, the model correctly reproduces the main features of the annual mean precipitation field and zonal mean wind in the stratosphere. Keywords: long-range forecasts, global atmosphere model, parameterizations of subgrid-scale processes


2021 ◽  
Author(s):  
Jonathan Day ◽  
Sarah Keeley ◽  
Kristian Mogensen ◽  
Steffen Tietsche ◽  
Linus Magnusson

<p>Dynamic sea ice and ocean have long been recognised as an important components in the Earth System Models used to generate climate change projections and more recently seasonal forecasts. However, the benefit of forecasts on the timescales of days to weeks has received less attention. Until recently it was assumed that sea-ice-ocean fields change so slowly that it is acceptable to keep them fixed in short and medium-range forecasts. However, at the ice edge the presence of sea ice dramatically influences surface fluxes, particularly when the overlying atmosphere is much colder than the open ocean so errors in the position of the sea ice, caused by simply persisting this field, have the potential to degrade atmospheric skill. To address this and similar issues, the European Centre for Medium-range Weather Forecasts (ECMWF) recently took the pioneering step of coupling a dynamic–thermodynamic sea ice-ocean model to the Integrated Forecast System, developing the first coupled medium-range forecasting system. This was a major step towards making ECMWF’s forecasts seamless across all timescales.</p><p>In this study we assess the benefits of including coupled sea-ice ocean processes in the medium-range by comparing set of ten-day forecasts with and without dynamic ice-ocean coupling, focussing on forecast performance at the edge of the sea ice and in the surrounding region. We demonstrate that dynamic coupling improves forecasts of the sea ice edge at all leadtimes. Further, the skill gained is larger during periods when the ice edge is advancing or retreating rapidly. We will also explore whether dynamic coupling has an impact on forecast skill in atmospheric parameters downstream of the ice edge.  </p>


2021 ◽  
Author(s):  
Leila Hieta ◽  
Mikko Partio ◽  
Marko Laine ◽  
Marja-Liisa Tuomola ◽  
Harri Hohti ◽  
...  

<p>Rapidly updating nowcasting system, Smartmet nowcast, has been developed at Finnish Meteorological Institute (FMI). The system combines information from multiple sources to operationally produce accurate and timely short range forecasts and a detailed description of the present weather to the end-users. The information sources combined are 1) Rapidly-updating high-resolution numerical weather prediction (NWP) MetCoOp nowcast (MNWC) forecast 2) radar-based nowcast 3) 10-day operational forecast. The Smartmet nowcast is currently produced for parameters 2-m temperature, 10-m wind speed, relative humidity, total cloud cover and accumulated 1-hour precipitation.</p><p>The system produces hourly updating nowcast information over the Scandinavian forecast domain and combines it seamlessly with the 10-day operational forecast information. Prior the combination a simple bias correction scheme based on recent forecast error information is applied to MNWC model analysis and forecast fields of 2-m temperature, relative humidity and 10-m wind speed. The blending of the nowcast and the 10-day operational forecast information is done using Optical-flow based image morphing method, which provides visually seamless forecasts for each forecast variable.</p><p>FMI has operationally produced Smartmet nowcast forecasts since September 2020. The validation of the data is in progress. The available results show that the Smartmet nowcast is improving the quality of short range forecasts and producing seamless and consistent forecasts. The method is also reducing the delay of forecast production. The Smartmet nowcast method will be automated in FMI forecast production in the near future.</p>


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