A Probabilistic Forecast Approach for Daily Precipitation Totals

2008 ◽  
Vol 23 (4) ◽  
pp. 659-673 ◽  
Author(s):  
Petra Friederichs ◽  
Andreas Hense
Keyword(s):  
Large Scale ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Reanalysis Data ◽  
Forecast System ◽  
Large Scale Circulation ◽  
Censored Quantile Regression ◽  
Circulation Patterns ◽  
Probabilistic Forecasts ◽  
Continuous Character

Abstract Commonly, postprocessing techniques are employed to calibrate a model forecast. Here, a probabilistic postprocessor is presented that provides calibrated probability and quantile forecasts of precipitation on the local scale. The forecasts are based on large-scale circulation patterns of the 12-h forecast from the NCEP high-resolution Global Forecast System (GFS). The censored quantile regression is used to estimate selected quantiles of the precipitation amount and the probability of the occurrence of precipitation. The approach accounts for the mixed discrete-continuous character of daily precipitation totals. The forecasts are verified using a new verification score for quantile forecasts, namely the censored quantile verification (CQV) score. The forecast approach is as follows: first, a canonical correlation is employed to correct systematic deviations in the GFS large-scale patterns compared with the NCEP–NCAR reanalysis or the 40-yr ECMWF Re-Analysis (ERA-40). Second, the statistical quantile model between the large-scale circulation and the local precipitation quantile is derived using NCEP and ERA-40 reanalysis data. Then, the statistical quantile model is applied to 12-h forecasts provided by the GFS forecast system. The probabilistic forecasts are reliable and the relative gain in performance of the quantile as well as the probability forecasts compared to the climatological forecasts range between 20% and 50%. The importance of the various parts of the postprocessing is assessed, and the performance is compared to forecasts based on the direct precipitation output from the ECMWF forecast system.

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 Development of SOM Daily Precipitation Statistical Downscaling Model And Its Applicability Assessment in North China

2021 ◽  
Author(s):  
Yongdi Wang ◽  
Xinyu Sun
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
North China ◽  
Daily Precipitation ◽  
Extreme Event ◽  
Precipitation Amount ◽  
Reanalysis Data ◽  
Frequency Difference ◽  
Downscaling Method ◽  
High Skill

Abstract A statistical downscaling method based on SOM which named SOM-SD is used over North China. It’s applicatibility by downscaling daily precipitation is evaluated. Indices are selected which represent the statistics of daily precipitation with regard to both precipitation amount (Prtot, SDII) and frequency (nr001), as well as extreme event (P95T, CWD, CDD). The large-scale predictors were extracted from the daily NCEP reanalysis data, while the predictand was high resolution gridded daily observed precipitation. A downscaling method based on SOM named SOM-SD was presented and evaluated. In evaluating, the frequency difference of wet-dry nodes is defined. And it is confirmed that there was a significant positive correlation between frequency difference and precipitation. The SOM-SD method displayed a high skill in reproducting the climatologic statistical properties of the observed precipitation. The value of BS is between 0 and 1.5×10-4. Sscore is between 0.8 and 1. The bias ranges are -7.4% and -11.6% for Prtot and SDII, -3.1days for nr001, +3.4% for P95T, -1.1 days for CWD and +3.5 days for CDD. Therefore, SOM-SD method works reasonably well.

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.

The influence of the main large-scale circulation patterns on wind power production in Portugal

2017 ◽  
Vol 102 ◽  
pp. 214-223 ◽  
Author(s):  
J.M. Correia ◽  
A. Bastos ◽  
M.C. Brito ◽  
R.M. Trigo
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Power Production ◽  
Large Scale Circulation ◽  
Circulation Patterns

Linking large-scale circulation patterns to the distribution of cold water corals along the eastern Rockall Bank (northeast Atlantic)

2020 ◽  
Vol 212 ◽  
pp. 103456
Author(s):  
Kirstin Schulz ◽  
Karline Soetaert ◽  
Christian Mohn ◽  
Laura Korte ◽  
Furu Mienis ◽  
...  
Keyword(s):  
Large Scale ◽  
Cold Water ◽  
Large Scale Circulation ◽  
Northeast Atlantic ◽  
Circulation Patterns

scholarly journals Probabilistic Heat Wave Forecast Based on a Large-Scale Circulation Pattern Using the TIGGE Data

2020 ◽  
Vol 35 (2) ◽  
pp. 367-377
Author(s):  
Hyun-Ju Lee ◽  
Woo-Seop Lee ◽  
Jong Ahn Chun ◽  
Hwa Woon Lee
Keyword(s):  
South Korea ◽  
Heat Wave ◽  
Large Scale ◽  
Heat Waves ◽  
Ensemble Prediction ◽  
Multimodel Ensemble ◽  
Large Scale Circulation ◽  
Circulation Patterns ◽  
Wave Forecast ◽  
Prediction Systems

Abstract Forecasting extreme events is important for having more time to prepare and mitigate high-impact events because those are expected to become more frequent, intense, and persistent around the globe in the future under the warming atmosphere. This study evaluates the probabilistic predictability of the heat wave index (HWI) associated with large-scale circulation patterns for predicting heat waves over South Korea. The HWI, reflecting heat waves over South Korea, was defined as the vorticity difference at 200 hPa between the South China Sea and northeast Asia. The forecast of up to 15 days from five ensemble prediction systems and the multimodel ensemble has been used to predict the probabilistic HWI during the summers of 2011–15. The ensemble prediction systems consist of different five operational centers, and the forecast skill of the probability of heat waves occurrence was assessed using the Brier skill score (BSS), relative operating characteristics (ROC), and reliability diagram. It was found that the multimodel ensemble is capable of better predicting the large-scale circulation patterns leading to heat waves over South Korea than any other single ensemble system through all forecast lead times. We concluded that the probabilistic forecast of the HWI has promise as a tool to take appropriate and timely actions to minimize the loss of lives and properties from imminent heat waves.

Large-scale circulation patterns and related rainfall in the Amazon Basin: a neuronal networks approach

2011 ◽  
Vol 38 (1-2) ◽  
pp. 121-140 ◽  
Author(s):  
Jhan Carlo Espinoza ◽  
Matthieu Lengaigne ◽  
Josyane Ronchail ◽  
Serge Janicot
Keyword(s):  
Large Scale ◽  
Amazon Basin ◽  
Neuronal Networks ◽  
Large Scale Circulation ◽  
Circulation Patterns

Predictability of large scale drivers leading intense Mediterranean cyclones

2020 ◽  
Author(s):  
M. Carmen Alvarez-Castro ◽  
Silvio Gualdi ◽  
Pascal Yiou ◽  
Mathieu Vrac ◽  
Robert Vautard ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Human Life ◽  
Heavy Precipitation ◽  
Mediterranean Area ◽  
Frequency Of Occurrence ◽  
Large Scale Circulation ◽  
Mediterranean Cyclones ◽  
Circulation Patterns ◽  
Anthropogenic Forcings

<p>Windstorms, extreme precipitations and instant floods seems to strike the Mediterranean area with increasing frequency. These events occur simultaneously during intense tropical-like Mediterranean cyclones. These intense Mediterranean cyclones are frequently associated with wind, heavy precipitation and changes in temperature, generating high risk situations such as flash floods and large-scale floods with significant impacts on human life and built environment. Although the dynamics of these phenomena is well understood, little is know about their climatology. It is therefore very difficult to make statements about the frequency of occurrence and its response to climate change. Thus, intense Mediterranean cyclones have many different physical aspects that can not be captured by a simple standard approach. </p><p>The first challenge of this work is to provide an extended catalogue and climatology of these phenomena by reconstructing a database of intense Mediterranean cyclones dating back up to 1969 using the satellite, the literature and reanalyses. Applying a method based on dynamical systems theory we analyse and attribute their future changes under different anthropogenic forcings by using future simulations within CMIP framework. Preliminary results show a decrease of the large-scale circulation patterns favoring intense Mediterranean cyclones in all the seasons except summer.</p>

scholarly journals Atmospheric Forcing of Debris Flows in the Southern Swiss Alps

2013 ◽  
Vol 52 (7) ◽  
pp. 1554-1560 ◽  
Author(s):  
Andrea Toreti ◽  
Michelle Schneuwly-Bollschweiler ◽  
Markus Stoffel ◽  
Jürg Luterbacher
Keyword(s):  
Time Series ◽  
Debris Flow ◽  
Debris Flows ◽  
Large Scale ◽  
Reanalysis Data ◽  
Swiss Alps ◽  
Classification Algorithms ◽  
Precipitation Series ◽  
Large Scale Circulation

AbstractThis article addresses the role of large-scale circulation and thermodynamical features in the release of past debris flows in the Swiss Alps by using classification algorithms, potential instability, and convective time scale. The study is based on a uniquely dense dendrogeomorphic time series of debris flows covering the period 1872–2008, reanalysis data, instrumental time series, and gridded hourly precipitation series (1992–2006) over the area. Results highlight the crucial role of synoptic and mesoscale forcing as well as of convective equilibrium on triggering rainfalls. Two midtropospheric synoptic patterns favor anomalous southwesterly flow toward the area and high potential instability. These findings imply a certain degree of predictability of debris-flow events and can therefore be used to improve existing alert systems.

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