scholarly journals Statistical Downscaling of Wintertime Temperatures over South Korea

2015 ◽  
Vol 32 (12) ◽  
pp. 2225-2241
Author(s):  
Seoyeon Lee ◽  
Kwang-Yul Kim
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
Weather Forecast ◽  
Error Variance ◽  
Reanalysis Data ◽  
Small Scale ◽  
Atmospheric Variability ◽  
Medium Range Weather Forecast ◽  
Cseof Analysis ◽  
Environmental Prediction

AbstractReanalysis data have global coverage and faithfully render large-scale phenomena. On the other hand, regional and small-scale characteristics of atmospheric variability are poorly resolved. In an attempt to improve reanalysis data for regional use, a statistical downscaling strategy is developed based on cyclostationary empirical orthogonal function (CSEOF) analysis. The developed algorithm is applied to the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data and to the European Centre for Medium-Range Weather Forecast (ECMWF) Interim Re-Analysis (ERA-Interim) data in order to produce winter temperatures at 60 Korea Meteorological Administration (KMA) stations over the Korean Peninsula. The developed downscaling algorithm is evaluated by predicting winter daily temperatures from 17 November to 16 March for 35 years (1979–2014). For validating the downscaling algorithm the jackknife method is used, in which winter daily temperature is predicted over a 1-yr period not used for training. This procedure is repeated for the entire data period. The mean and variance of the resulting downscaled temperatures match reasonably well with those of the KMA measurements. Validation based on correlation and error variance shows that the temperatures at 60 KMA stations are faithfully reproduced based on coarse reanalysis data. The utility of this technique for downscaling model predictions based on future scenarios is also addressed.

scholarly journals Detection and thermal description of medicanes from numerical simulation

2014 ◽  
Vol 14 (5) ◽  
pp. 1059-1070 ◽  
Author(s):  
M. A. Picornell ◽  
J. Campins ◽  
A. Jansà
Keyword(s):  
Numerical Simulation ◽  
Thermal Structure ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Small Scale ◽  
Medium Range Weather Forecast ◽  
Warm Core ◽  
Strong Winds ◽  
Nwp Model ◽  
Ecmwf Model

Abstract. Tropical-like cyclones rarely affect the Mediterranean region but they can produce strong winds and heavy precipitations. These warm-core cyclones, called MEDICANES (MEDIterranean hurriCANES), are small in size, develop over the sea and are infrequent. For these reasons, the detection and forecast of medicanes are a difficult task and many efforts have been devoted to identify them. The goals of this work are to contribute to a proper description of these structures and to develop some criteria to identify medicanes from numerical weather prediction (NWP) model outputs. To do that, existing methodologies for detecting, characterizating and tracking cyclones have been adapted to small-scale intense cyclonic perturbations. First, a mesocyclone detection and tracking algorithm has been modified to select intense cyclones. Next, the parameters that define the Hart's cyclone phase diagram are tuned and calculated to examine their thermal structure. Four well-known medicane events have been described from numerical simulation outputs of the European Centre for Medium-Range Weather Forecast (ECMWF) model. The predicted cyclones and their evolution have been validated against available observational data and numerical analyses from the literature.

scholarly journals Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods

2007 ◽  
Vol 4 (5) ◽  
pp. 3413-3440 ◽  
Author(s):  
E. P. Maurer ◽  
H. G. Hidalgo
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
Impact Analysis ◽  
Climate Model ◽  
Daily Precipitation ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Temperature Extremes ◽  
Daily Data

Abstract. Downscaling of climate model data is essential to most impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8-degree (approximately 140 km² per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950–1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit some skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.

Atmospheric forcing of coastal polynyas in the south-western Weddell Sea

2015 ◽  
Vol 27 (4) ◽  
pp. 388-402 ◽  
Author(s):  
Verena Haid ◽  
Ralph Timmermann ◽  
Lars Ebner ◽  
Günther Heinemann
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Weddell Sea ◽  
Small Scale ◽  
Atmospheric Conditions ◽  
Atmospheric Forcing ◽  
The South ◽  
Ice Production

AbstractThe development of coastal polynyas, areas of enhanced heat flux and sea ice production strongly depend on atmospheric conditions. In Antarctica, measurements are scarce and models are essential for the investigation of polynyas. A robust quantification of polynya exchange processes in simulations relies on a realistic representation of atmospheric conditions in the forcing dataset. The sensitivity of simulated coastal polynyas in the south-western Weddell Sea to the atmospheric forcing is investigated with the Finite-Element Sea ice-Ocean Model (FESOM) using daily NCEP/NCAR reanalysis data (NCEP), 6 hourly Global Model Europe (GME) data and two different hourly datasets from the high-resolution Consortium for Small-Scale Modelling (COSMO) model. Results are compared for April to August in 2007–09. The two coarse-scale datasets often produce the extremes of the data range, while the finer-scale forcings yield results closer to the median. The GME experiment features the strongest winds and, therefore, the greatest polynya activity, especially over the eastern continental shelf. This results in higher volume and export of High Salinity Shelf Water than in the NCEP and COSMO runs. The largest discrepancies between simulations occur for 2008, probably due to differing representations of the ENSO pattern at high southern latitudes. The results suggest that the large-scale wind field is of primary importance for polynya development.

scholarly journals Climatology and Trend of Severe Drought Events in the State of Sao Paulo, Brazil, during the 20th Century

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 190 ◽  
Author(s):  
Luiz Gozzo ◽  
Doris Palma ◽  
Maria Custodio ◽  
Jeferson Machado
Keyword(s):  
20Th Century ◽  
Linear Trend ◽  
Weather Forecast ◽  
Reanalysis Data ◽  
Sao Paulo ◽  
The State ◽  
Economic Consequences ◽  
Severe Drought ◽  
São Paulo ◽  
Medium Range Weather Forecast

Drought is a natural hazard with critical societal and economic consequences to millions of people around the world. In this paper, we present the climatology of severe drought events that occurred during the 20th century in the region of Sao Paulo, Brazil. To account for the effects of rainfall deficit and changes in temperature at a climatic timescale, we chose the Standardized Precipitation Evapotranspiration Index (SPEI) to identify severe droughts over the city of Sao Paulo, and the eastern and central-western regions of the state. Events were identified using weather station data and European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis data, in order to assess the representation of drought periods in both datasets. Results show that the reanalysis seems suitable to represent the number of events and their mean duration, severity and intensity, but the timing and characteristics of individual events are not well reproduced. The correlation between observation and reanalysis SPEI time series is low to moderate in all cases. A linear trend analysis between 1901 and 2010 shows a tendency of increasing (decreasing) severe drought events in the central and western (eastern) Sao Paulo state, according to observational data. This is in agreement with previous findings, and the reanalysis presents this same signal. The weakened trend values in the reanalysis may be associated with issues in representing precipitation in this dataset.

scholarly journals A Global Atmospheric Analysis Dataset Downscaled from the NCEP–DOE Reanalysis

2012 ◽  
Vol 25 (7) ◽  
pp. 2527-2534 ◽  
Author(s):  
Jung-Eun Kim ◽  
Song-You Hong
Keyword(s):  
Large Scale ◽  
Hydrological Cycle ◽  
Analysis Data ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Department Of Energy ◽  
Quality Analysis ◽  
Resolution Model ◽  
Decadal Trend ◽  
Environmental Prediction

Abstract A global atmospheric analysis dataset is constructed via a spectral nudging technique. The 6-hourly National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) reanalysis from January 1979 to February 2011 is utilized to force large-scale information, whereas a higher-resolution structure is resolved by a global model with improved physics. The horizontal resolution of the downscaled data is about 100 km, twice that of the NCEP–DOE reanalysis. A comparison of the 31-yr downscaled data with reanalysis data and observations reveals that the downscaled precipitation climatology is improved by correcting inherent biases in the lower-resolution reanalysis, and large-scale patterns are preserved. In addition, it is found that global downscaling is an efficient way to generate high-quality analysis data due to the use of a higher-resolution model with improved physics. The uniqueness of the obtained data lies in the fact that an undesirable decadal trend in the analysis due to a change in the amount of observations used in reanalysis is avoided. As such, a downscaled dataset may be used to investigate changes in the hydrological cycle and related mechanisms.

scholarly journals Widespread polar stratospheric ice clouds in the 2015/2016 Arctic winter – Implications for ice nucleation

2017 ◽  
Author(s):  
Christiane Voigt ◽  
Andreas Dörnbrack ◽  
Martin Wirth ◽  
Silke M. Groß ◽  
Michael C. Pitts ◽  
...  
Keyword(s):  
Weather Forecast ◽  
Satellite Observation ◽  
Reanalysis Data ◽  
Ice Nucleation ◽  
The Arctic ◽  
Orthogonal Polarization ◽  
Data Set ◽  
Ice Clouds ◽  
Medium Range Weather Forecast ◽  
Cold Temperatures

Abstract. Low planetary wave activity led to a stable vortex with exceptionally cold temperatures in the 2015/2016 Arctic winter. Extended areas with temperatures below the ice frost point Tice persisted over weeks in the Arctic stratosphere as derived from the 36-years temperature climatology of the ERA-Interim reanalysis data set of the European Center for Medium Range Weather Forecast ECMWF. These extreme conditions promoted the formation of widespread polar stratospheric ice clouds (ice PSCs). The space-borne Cloud-Aerosol Lidar with Orthogonal Polarization CALIOP instrument onboard the CALIPSO satellite (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) continuously measured ice PSCs for about a month with maximum extensions of up to 2 × 106 km2 in the stratosphere. On 22 January 2016, the WALES (Water Vapor Lidar Experiment in Space – airborne demonstrator) lidar onboard the High Altitude and Long Range Research Aircraft HALO detected an ice PSC with a horizontal length of more than 1400 km. The ice PSC extended between 18 and 24 km altitude and was surrounded by nitric acid trihydrate (NAT) particles, supercooled ternary solution (STS) droplets and particle mixtures. The ice PSC occurrence in the backscatter ratio to depolarization optical space spanned by WALES observations is best matched by defining the inverse backscatter ratio of 0.3 as 1/Rice|NAT threshold between ice and NAT cloud regions. In addition, the histogram clearly shows two distinct branches in ice PSC occurrence, indicative for two ice formation pathways. In addition to ice nucleation in STSm with meteoric dust inclusions, ice nucleation on pre-existing NAT may play a role in the Arctic winter 2015/2016. This hypothesis is supported by differences in the ECMWF trajectory analysis for the two ice branches. The observation of widespread Arctic ice PSCs can advance our understanding of ice nucleation in cold polar and tropical latitudes. It further provides a new observational data base for the parameterization of ice nucleation schemes in atmospheric models.

Probabilistic fire-danger forecasting: A framework for week-two forecasts using statistical post-processing techniques and the Global ECMWF Fire Forecast System (GEFF)

2021 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Francesca Di Giuseppe ◽  
Christopher Barnard ◽  
Thomas M. Hamill ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Reanalysis Data ◽  
Post Processing ◽  
Medium Range Weather Forecast ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Systematic Biases ◽  
Processing Techniques

AbstractWildfire guidance two weeks ahead is needed for strategic planning of fire mitigation and suppression. However, fire forecasts driven by meteorological forecasts from numerical weather prediction models inherently suffer from systematic biases. This study uses several statistical-postprocessing methods to correct these biases and increase the skill of ensemble fire forecasts over the contiguous United States 8–14 days ahead. We train and validate the post-processing models on 20 years of European Centre for Medium-range Weather Forecast (ECMWF) reforecasts and ERA5 reanalysis data for 11 meteorological variables related to fire, such as surface temperature, wind speed, relative humidity, cloud cover, and precipitation. The calibrated variables are then input to the Global ECMWF Fire Forecast (GEFF) system to produce probabilistic forecasts of daily fire-indicators which characterize the relationships between fuels, weather, and topography. Skill scores show that the post-processed forecasts overall have greater positive skill at Days 8–14 relative to raw and climatological forecasts. It is shown that the post-processed forecasts are more reliable at predicting above- and below-normal probabilities of various fire indicators than the raw forecasts and that the greatest skill for Days 8–14 is achieved by aggregating forecast days together.

scholarly journals Statistical Downscaling of Extreme Precipitation Events Using Censored Quantile Regression

2007 ◽  
Vol 135 (6) ◽  
pp. 2365-2378 ◽  
Author(s):  
P. Friederichs ◽  
A. Hense
Keyword(s):  
Quantile Regression ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Daily Precipitation ◽  
Skill Score ◽  
Reanalysis Data ◽  
Conditional Quantile ◽  
Conditional Quantiles ◽  
Censored Quantile Regression ◽  
Extreme Precipitation Events

Abstract A statistical downscaling approach for extremes using censored quantile regression is presented. Conditional quantiles of station data (e.g., daily precipitation sums) in Germany are estimated by means of the large-scale circulation as represented by the NCEP reanalysis data. It is shown that a mixed discrete–continuous response variable, such as a daily precipitation sum, can be statistically modeled by a censored variable. Furthermore, a conditional quantile skill score is formulated to assess the relative gain of a quantile forecast compared with a reference forecast. Just like multiple regression for expectation values, quantile regression provides a tool to formulate a model output statistics system for extremal quantiles.

scholarly journals Evaluation of Forecast Performance for Four Meteorological Models in Summer Over Northwestern China

2021 ◽  
Vol 9 ◽  
Author(s):  
Yuanpu Liu ◽  
Tiejun Zhang ◽  
Haixia Duan ◽  
Jing Wu ◽  
Dingwen Zeng ◽  
...  
Keyword(s):  
Large Scale ◽  
Numerical Models ◽  
Northwestern China ◽  
Precipitation Forecast ◽  
Small Scale ◽  
Prediction Errors ◽  
Prediction System ◽  
China Meteorological Administration ◽  
Forecast Performance ◽  
Medium Range Weather Forecast

At present, numerical models, which have been used for forecasting services in northwestern China, have not been extensively evaluated. We used national automatic ground station data from summer 2016 to test and assess the forecast performance of the high-resolution global European Centre for Medium-Range Weather Forecast (ECMWF) model, the mesoscale Northwestern Mesoscale Numerical Prediction System (NW-MNPS), the global China Meteorological Administration T639 model, and the mesoscale Global/Regional Assimilation and Prediction System (GRAPES) model over northwestern China. The root mean square error (RMSE) of the 2-m temperature forecast by ECMWF was the lowest, while that by T639 was the highest. The distribution of RMSE for each model forecast was similar to that of the difference between the modeled and observed terrain. The RMSE of the 10-m wind speed forecast was lower for the global ECMWF and T639 models and higher for the regional NW-MNPS and GRAPES models. The 24-h precipitation forecast was generally higher than observed for each model, with NW-MNPS having the highest score for light rain and heavy storm rain, ECMWF for medium and heavy rain, and T639 for storm rain. None of the models could forecast small-scale and high-intensity precipitation, but they could forecast large-scale precipitation. Overall, ECMWF had the best stability and smallest prediction errors, followed by NW-MNPS, T639, and GRAPES.

scholarly journals Analysis of Vortex Development over Eastern Indian Ocean using Potential Vorticity

2021 ◽  
Vol 893 (1) ◽  
pp. 012003
Author(s):  
R P Damayanti ◽  
N J Trilaksono ◽  
M R Abdillah
Keyword(s):  
Indian Ocean ◽  
Potential Vorticity ◽  
Vortex Motion ◽  
Weather Forecast ◽  
Reanalysis Data ◽  
Eastern Indian Ocean ◽  
Medium Range Weather Forecast ◽  
Circulation Patterns ◽  
Ocean Region ◽  
Flooding Events

Abstract A vortex phenomenon may have a significant influence, especially on wind circulation patterns and extreme weather in Indonesia. The formation of the vortex, initially located over the eastern part of the Indian Ocean has drawn attention due to the highest frequency of its occurrence and as the source of the vortex over the Indonesian region. Vortices generated in this region is also suspected as one of contributing factor for flooding events at Jakarta in 2002 and 2007, studying both formation and development mechanism of these vortices is essential. The evolution of vortex development is investigated to characterize the vortex motion and development pattern in the Eastern Indian Ocean region. The study was conducted for 17 years starting from 1998 to 2016 on every December-January-February (DJF) period using ECMWF (European Center for Medium-Range Weather Forecast) ERA-Interim Reanalysis data. The analysis of vortex evolution was conducted for each event using a composite evolution of potential vorticity anomalies in the isentropic layer. The result shows 84 vortex systems identified with three characteristic patterns of vortex movement that occurred during 295 days of the observation period. Composite analysis of potential vorticity anomalies shows that the initial formation of vortices in the Eastern Indian Ocean is related to the emergence of negative potential vorticity anomalies from the west, which subsequently forming the vortices.

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