scholarly journals Dynamically vs. empirically downscaled medium-range precipitation forecasts

2009 ◽  
Vol 13 (9) ◽  
pp. 1649-1658 ◽  
Author(s):  
G. Bürger
Keyword(s):  
Dynamical System ◽  
Lead Time ◽  
Regional Model ◽  
The Other ◽  
Medium Range Forecast ◽  
Mountainous Catchments ◽  
Skill Scores ◽  
Medium Range ◽  
Ecmwf Model ◽  
Empirical System

Abstract. For three small, mountainous catchments in Germany two medium-range forecast systems are compared that predict precipitation for up to 5 days in advance. One system is composed of the global German weather service (DWD) model, GME, which is dynamically downscaled using the COSMO-EU regional model. The other system is an empirical (expanded) downscaling of the ECMWF model IFS. Forecasts are verified against multi-year daily observations, by applying standard skill scores to events of specified intensity. All event classes are skillfully predicted by the empirical system for up to five days lead time. For the available prediction range of one to two days it is superior to the dynamical system.

scholarly journals Dynamically vs. empirically downscaled medium-range precipitation forecasts

2009 ◽  
Vol 6 (2) ◽  
pp. 3517-3542
Author(s):  
G. Bürger
Keyword(s):  
Dynamical System ◽  
Lead Time ◽  
Regional Model ◽  
The Other ◽  
Medium Range Forecast ◽  
Mountainous Catchments ◽  
Skill Scores ◽  
Medium Range ◽  
Ecmwf Model ◽  
Empirical System

Abstract. For three small, mountainous catchments in Germany two medium-range forecast systems are compared that predict precipitation for up to 5 days in advance. One system is composed of the global German weather service (DWD) model, GME, which is dynamically downscaled using the COSMO-EU regional model. The other system is an empirical (expanded) downscaling of the ECMWF model IFS. Forecasts are verified against multi-year daily observations, by applying standard skill scores to events of specified intensity. All event classes are skillfully predicted by the empirical system for up to five days lead time. For the available prediction range of one to two days it is superior to the dynamical system.

scholarly journals The Skill of ECMWF Medium-Range Forecasts during the Year of Tropical Convection 2008

2010 ◽  
Vol 138 (10) ◽  
pp. 3787-3805 ◽  
Author(s):  
Arindam Chakraborty
Keyword(s):  
Surface Temperature ◽  
Diurnal Cycle ◽  
Lead Time ◽  
Tropical Convection ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Near Surface ◽  
Forecast Lead Time ◽  
Medium Range ◽  
Ecmwf Model

Abstract This study uses the European Centre for Medium-Range Weather Forecasts (ECMWF) model-generated high-resolution 10-day-long predictions for the Year of Tropical Convection (YOTC) 2008. Precipitation forecast skills of the model over the tropics are evaluated against the Tropical Rainfall Measuring Mission (TRMM) estimates. It has been shown that the model was able to capture the monthly to seasonal mean features of tropical convection reasonably. Northward propagation of convective bands over the Bay of Bengal was also forecasted realistically up to 5 days in advance, including the onset phase of the monsoon during the first half of June 2008. However, large errors exist in the daily datasets especially for longer lead times over smaller domains. For shorter lead times (less than 4–5 days), forecast errors are much smaller over the oceans than over land. Moreover, the rate of increase of errors with lead time is rapid over the oceans and is confined to the regions where observed precipitation shows large day-to-day variability. It has been shown that this rapid growth of errors over the oceans is related to the spatial pattern of near-surface air temperature. This is probably due to the one-way air–sea interaction in the atmosphere-only model used for forecasting. While the prescribed surface temperature over the oceans remain realistic at shorter lead times, the pattern and hence the gradient of the surface temperature is not altered with change in atmospheric parameters at longer lead times. It has also been shown that the ECMWF model had considerable difficulties in forecasting very low and very heavy intensity of precipitation over South Asia. The model has too few grids with “zero” precipitation and heavy (>40 mm day−1) precipitation. On the other hand, drizzle-like precipitation is too frequent in the model compared to that in the TRMM datasets. Further analysis shows that a major source of error in the ECMWF precipitation forecasts is the diurnal cycle over the South Asian monsoon region. The peak intensity of precipitation in the model forecasts over land (ocean) appear about 6 (9) h earlier than that in the observations. Moreover, the amplitude of the diurnal cycle is much higher in the model forecasts compared to that in the TRMM estimates. It has been seen that the phase error of the diurnal cycle increases with forecast lead time. The error in monthly mean 3-hourly precipitation forecasts is about 2–4 times of the error in the daily mean datasets. Thus, effort should be given to improve the phase and amplitude forecast of the diurnal cycle of precipitation from the model.

Impact of non-Arctic observations on the AROME-Arctic regional model

2020 ◽  
Author(s):  
Rogerr Randriamampianina
Keyword(s):  
Regional Model ◽  
Satellite Observations ◽  
Lateral Boundary ◽  
Regional Data ◽  
Weather Forecasts ◽  
Lateral Boundary Conditions ◽  
Skill Scores ◽  
Medium Range ◽  
Considerable Impact ◽  
The Impact

<p>In the framework of the Applicate project (https://applicate.eu), ECMWF (European Centre for Medium-Range Weather Forecasts) performed global (Bormann et al. 2019) and Arctic (Lawrence et al. 2019) observing system experiments. Use of the results of these experiments as lateral boundary conditions (LBC) for our regional model opens opportunity to study the following: 1) the impact of observations through regional data assimilation (DA); 2) the impact of observations that are assimilated in a global model through LBC in a regional model; 3) the impact of global loss of observations in a regional model; and 4) the impact of non-Arctic observations in an Arctic regional model.</p><p>In the framework of the Alertness project, we performed experiments for the two special observation periods (SOP) 1 and 2 and found considerable impact (significant for some cases) of both conventional and satellite observations through both regional DA and LBC. So far, the impact of non-Arctic observations on our Arctic regional model AROME-Arctic analyses and forecasts was checked during SOP1 with microwave radiance only. The impact was found to be positive, especially on day-2 forecasts.</p><p>In this presentation, the impact of other non-Arctic observations (conventional and satellite) on our regional model AROME-Arctic will be discussed through different forecast skill scores verification.</p>

scholarly journals Some Characteristics of Model-Predicted Precipitation during the Summer Monsoon over India

2005 ◽  
Vol 44 (3) ◽  
pp. 324-339 ◽  
Author(s):  
B. K. Basu
Keyword(s):  
Summer Monsoon ◽  
Lead Time ◽  
Precipitation Pattern ◽  
Spatial Average ◽  
Spatial Averaging ◽  
Forecast Lead Time ◽  
Medium Range ◽  
Ecmwf Model ◽  
Rainy Days ◽  
Range Forecasts

Abstract For the summer monsoon seasons of 1995, 1996, and 1997 the day-1 to day-4 forecasts of precipitation from both the National Centre for Medium Range Weather Forecasting (NCMRWF) and the European Centre for Medium-Range Forecasts (ECMWF) models reproduce the main features of the observed precipitation pattern when averaged over the whole season. On average, less than 30% of all rain gauge stations in India report rain on a given day during the monsoon season. The number of observed rainy days increases to 41% after spatial averaging over ECMWF model grid boxes and to 50% after spatial averaging over NCMRWF model grid boxes. The NCMRWF model forecasts have 10%–15% more rainy days, mostly in the light or moderate precipitation categories, when compared with the spatial average of observed values. Seasonal accumulated values of all of India’s average precipitation show a slight increase with the forecast lead time for the NCMRWF model and a small decrease for the ECMWF model. The weekly accumulated values of forecast precipitation from both models, averaged over the whole of India, are in good phase relationship (∼0.9 in most cases) with the observed value for forecasts with a lead time up to day 4. Values of statistical parameters, based on the frequency of occurrence in various classes, indicate that the NCMRWF model has some skill in predicting precipitation over India during the summer monsoon. The NCMRWF model forecasts have higher trend correlation with the observed precipitation over India than do the ECMWF model forecasts. The mean error in precipitation is, however, much less in the ECMWF model forecasts, and the spatial distribution of seasonal average medium-range forecasts of ECMWF is closer to that observed along the west coast mountain ridgeline.

An Analysis of the Accuracy of 120-h Predictions by the National Meteorological Center's Medium-Range Forecast Model

1994 ◽  
Vol 9 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Mary A. Bedrick ◽  
Anthony J. Cristaldi ◽  
Stephen J. Colucci ◽  
Daniel S. Wilks
Keyword(s):  
Forecast Model ◽  
Medium Range Forecast ◽  
Medium Range

A SIMPLE OBSERVER OF THE HYPERCHAOTIC RÖSSLER SYSTEM

2010 ◽  
Vol 24 (22) ◽  
pp. 4325-4331
Author(s):  
XING-YUAN WANG ◽  
JUN-MEI SONG
Keyword(s):  
Dynamical System ◽  
Time Domain ◽  
Global Exponential Stability ◽  
Observer Design ◽  
The Other ◽  
State Observation ◽  
Rossler System ◽  
Error System ◽  
Rössler System ◽  
The Time Domain

This paper studies the hyperchaotic Rössler system and the state observation problem of such a system being investigated. Based on the time-domain approach, a simple observer for the hyperchaotic Rössler system is proposed to guarantee the global exponential stability of the resulting error system. The scheme is easy to implement and different from the other observer design that it does not need to transmit all signals of the dynamical system. It is proved theoretically, and numerical simulations show the effectiveness of the scheme finally.

scholarly journals The Impact of Convective Momentum Transport on Tropical Cyclone Track Forecasts Using the Emanuel Cumulus Parameterization

2007 ◽  
Vol 135 (4) ◽  
pp. 1195-1207 ◽  
Author(s):  
Timothy F. Hogan ◽  
Randal L. Pauley
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Tropical Cyclones ◽  
Momentum Transport ◽  
Cumulus Parameterization ◽  
Transport Parameter ◽  
Medium Range Forecast ◽  
Medium Range ◽  
Convective Momentum Transport ◽  
The Impact

Abstract The influence of convective momentum transport (CMT) on tropical cyclone (TC) track forecasts is examined in the Navy Operational Global Atmospheric Prediction System (NOGAPS) with the Emanuel cumulus parameterization. Data assimilation and medium-range forecast experiments show that for 35 tropical cyclones during August and September 2004 the inclusion of CMT in the cumulus parameterization significantly improves the TC track forecasts. The tests show that the track forecasts are very sensitive to the magnitude of the Emanuel parameterization’s convective momentum transport parameter, which controls the CMT tendency returned by the parameterization. While the overall effect of this formulation of CMT in NOGAPS data assimilation/medium-range forecasts results in the surface pressure of tropical cyclones being less intense (and more consistent with the analysis), the parameterization is not equivalent to a simple diffusion of winds in the presence of convection. This is demonstrated by two data assimilation/medium-range forecast tests in which a vertical diffusion algorithm replaces the CMT. Two additional data assimilation/medium-range forecast experiments were conducted to test whether the skill increase primarily comes from the CMT in the immediate vicinity of the tropical cyclones. The results show that the inclusion of the CMT calculation in the vicinity of the TC makes the largest contribution to the increase in forecast skill, but the general contribution of CMT away from the TC also plays an important role.

Short- and Medium-range Prediction of Relativistic Electron Flux in the Earth’s Outer Radiation Belt by Machine Learning Methods

2021 ◽  
Vol 3 ◽  
pp. 47-57
Author(s):  
I. N. Myagkova ◽  
◽  
V. R. Shirokii ◽  
Yu. S. Shugai ◽  
O. G. Barinov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Radiation Belt ◽  
Gradient Boosting ◽  
Relativistic Electrons ◽  
Learning Methods ◽  
Outer Radiation Belt ◽  
Machine Learning Methods ◽  
The Earth ◽  
Skill Scores ◽  
Medium Range

The ways are studied to improve the quality of prediction of the time series of hourly mean fluxes and daily total fluxes (fluences) of relativistic electrons in the outer radiation belt of the Earth 1 to 24 hours ahead and 1 to 4 days ahead, respectively. The prediction uses an approximation approach based on various machine learning methods, namely, artificial neural networks (ANNs), decision tree (random forest), and gradient boosting. A comparison of the skill scores of short-range forecasts with the lead time of 1 to 24 hours showed that the best results were demonstrated by ANNs. For medium-range forecasting, the accuracy of prediction of the fluences of relativistic electrons in the Earth’s outer radiation belt three to four days ahead increases significantly when the predicted values of the solar wind velocity near the Earth obtained from the UV images of the Sun of the AIA (Atmospheric Imaging Assembly) instrument of the SDO (Solar Dynamics Observatory) are included to the list of the input parameters.

scholarly journals Medium-Range Convection-Allowing Ensemble Forecasts with a Variable-Resolution Global Model

2019 ◽  
Vol 147 (8) ◽  
pp. 2997-3023 ◽  
Author(s):  
Craig S. Schwartz
Keyword(s):  
High Resolution ◽  
Ensemble Forecast ◽  
The Other ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Variable Resolution ◽  
Cell Spacing ◽  
Medium Range ◽  
Potential Benefits ◽  
Convective Mode

Abstract Two sets of global, 132-h (5.5-day), 10-member ensemble forecasts were produced with the Model for Prediction Across Scales (MPAS) for 35 cases in April and May 2017. One MPAS ensemble had a quasi-uniform 15-km mesh while the other employed a variable-resolution mesh with 3-km cell spacing over the conterminous United States (CONUS) that smoothly relaxed to 15 km over the rest of the globe. Precipitation forecasts from both MPAS ensembles were objectively verified over the central and eastern CONUS to assess the potential benefits of configuring MPAS with a 3-km mesh refinement region for medium-range forecasts. In addition, forecasts from NCEP’s operational Global Ensemble Forecast System were evaluated and served as a baseline against which to compare the experimental MPAS ensembles. The 3-km MPAS ensemble most faithfully reproduced the observed diurnal cycle of precipitation throughout the 132-h forecasts and had superior precipitation skill and reliability over the first 48 h. However, after 48 h, the three ensembles had more similar spread, reliability, and skill, and differences between probabilistic precipitation forecasts derived from the 3- and 15-km MPAS ensembles were typically statistically insignificant. Nonetheless, despite fewer benefits of increased resolution for spatial placement after 48 h, 3-km ensemble members explicitly provided potentially valuable guidance regarding convective mode throughout the 132-h forecasts while the other ensembles did not. Collectively, these results suggest both strengths and limitations of medium-range high-resolution ensemble forecasts and reveal pathways for future investigations to improve understanding of high-resolution global ensembles with variable-resolution meshes.

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