Daily Precipitation Sums Research Articles

New gridded daily climatology of Finland: Permutation‐based uncertainty estimates and temporal trends in climate

AbstractLong‐term time series of key climate variables with a relevant spatiotemporal resolution are essential for environmental science. Moreover, such spatially continuous data, based on weather observations, are commonly used in, e.g., downscaling and bias correcting of climate model simulations. Here we conducted a comprehensive spatial interpolation scheme where seven climate variables (daily mean, maximum, and minimum surface air temperatures, daily precipitation sum, relative humidity, sea level air pressure, and snow depth) were interpolated over Finland at the spatial resolution of 10 × 10 km2. More precisely, (1) we produced daily gridded time series (FMI_ClimGrid) of the variables covering the period of 1961–2010, with a special focus on evaluation and permutation‐based uncertainty estimates, and (2) we investigated temporal trends in the climate variables based on the gridded data. National climate station observations were supplemented by records from the surrounding countries, and kriging interpolation was applied to account for topography and water bodies. For daily precipitation sum and snow depth, a two‐stage interpolation with a binary classifier was deployed for an accurate delineation of areas with no precipitation or snow. A robust cross‐validation indicated a good agreement between the observed and interpolated values especially for the temperature variables and air pressure, although the effect of seasons was evident. Permutation‐based analysis suggested increased uncertainty toward northern areas, thus identifying regions with suboptimal station density. Finally, several variables had a statistically significant trend indicating a clear but locally varying signal of climate change during the last five decades.

Identification and Climatology of Alpine Pumping from a Regional Climate Simulation

The thermally driven circulation between the European Alps and the alpine foreland - named Alpine pumping – occurs regularly under clear and calm weather conditions. While previous studies focused on the impact of Alpine pumping on moist convection and transport of air pollutants, this study was motivated by its ventilation effect for Munich, located about 50 km north of the Alps in undulating and only slightly inclined terrain, where local thermal circulations are weak. Hourly data from a reanalysis driven regional climate simulation with COSMO-CLM model for the period 1989 to 2008 were analysed to identify days with Alpine pumping and to determine the mean diurnal characteristics of this regional thermal circulation. Four literature derived combinations of meteorological criteria were tested to identify days favorable for Alpine pumping from COSMO-CLM results. The first criterion selects days with a daily sum of solar radiation ≥20 MJ/m2 and has been used in an earlier observational study. On average 60 d/y are fulfilling the criterion in the model simulation, which compares well to the 67 d/y determined from observations. The other three criteria combinations consider a maximum wind velocity at 850 hPa, a maximum daily precipitation sum, and/or a maximum mean cloud cover. The mean annual number of selected days is lower for these criteria combinations and ranges between 20 and 52. Diurnal wind reversals occur on 77 to 81% of the selected days, depending on the criteria combination The daily solar radiation sum of 20 MJ/m2 is only exceeded during April to September, while days satisfying the criteria combinations without the radiation threshold occur all year round. In agreement with observations, the simulated regional thermally driven wind field extends up to ~100 km north of the Alps with average near-surface wind speeds of 0.5-1.5 m/s in the Munich area. With increasing distance from the Alps, the diurnal cycle of Alpine pumping is delayed by up to 3 hours. The simulated mean depth of the daytime inflow layer ranges between 500 m and 1500 m, whereas the depth of the nocturnal outflow layer typically reaches up to a few hundred meters.

Heavy rains in Brno Region (The Czech Republic)

Abstract This study evaluates occurrence of the heavy rains at urban areas on the example of Brno agglomeration. Extreme precipitation events are studied with the help of 1-minute records from three urban sites in the Brno and one site in the rural region. Additionally data from nine Hellmann rain gauges from Brno and surroundings were used. The event of torrential rains was evaluated according to Wussow’s method. The study found that torrential rains occurred in Brno in the season from April to November. The fifteen-year long observation of the phenomenon at Brno-Žabovřesky station shows varying frequency of torrential rainfall - from none in 2007 to four in 2004 and 2010. The most extremal was catastrophic downpour on July 15, 2009 when precipitation sum reached total up to 42.7 mm in 52 minutes and the maximal rainfall intensity was 4.1 mm/min. This phenomenon occurs only one day after downpour on July 14, 2009. The overall number of torrential episodes is not very high, but it is compensated by the fact that they can repeat in a relatively short period of time, due to specific synoptic conditions. Maximum rainfall intensity during torrential rains reaches values of about 3 mm / min and the average proportion of torrential rain in the daily precipitation sum is around 70%. The phenomenon of heavy rains represents a significant problem for densely populated urban areas because such extreme rainfall has a large impact on the hydrological system of cities.

Implementation and validation of a Wilks-type multi-site daily precipitation generator over a typical Alpine river catchment

Abstract. Many climate impact assessments require high-resolution precipitation time series that have a spatio-temporal correlation structure consistent with observations, for simulating either current or future climate conditions. In this respect, weather generators (WGs) designed and calibrated for multiple sites are an appealing statistical downscaling technique to stochastically simulate multiple realisations of possible future time series consistent with the local precipitation characteristics and their expected future changes. In this study, we present the implementation and validation of a multi-site daily precipitation generator re-built after the methodology described in Wilks (1998). The generator consists of several Richardson-type WGs run with spatially correlated random number streams. This study aims at investigating the capabilities, the added value and the limitations of the precipitation generator for a typical Alpine river catchment in the Swiss Alpine region under current climate. The calibrated multi-site WG is skilful at individual sites in representing the annual cycle of the precipitation statistics, such as mean wet day frequency and intensity as well as monthly precipitation sums. It reproduces realistically the multi-day statistics such as the frequencies of dry and wet spell lengths and precipitation sums over consecutive wet days. Substantial added value is demonstrated in simulating daily areal precipitation sums in comparison to multiple WGs that lack the spatial dependency in the stochastic process. Limitations are seen in reproducing daily and multi-day extreme precipitation sums, observed variability from year to year and in reproducing long dry spell lengths. Given the performance of the presented generator, we conclude that it is a useful tool to generate precipitation series consistent with the mean climatic aspects and likely helpful to be used as a downscaling technique for climate change scenarios.

Využití denních teplotních extrémů a úhrnu srážek k odhadu globálního slunečního záření

Two methods for estimating daily global solar radiation (RG) based on the daily temperature extremes and precipitation sum are compared in the study. All parameters necessary for application of both methods were derived either from literature or from climatic characteristics easily available at the given meteorological stations excluding need for measured RG data. The performance of both methods was assessed with a help of meteorological database including 4 stations in the Czech Republic (data were provided by the Czech Hydrometeorological Institute) and 6 in Austria (data provided by the Austrian Weather Service) containing in total 41 640 observational day. For each day in the database observed daily sum of RG, daily maximum and minimum temperatures and precipitation sum were available. Coefficient of determination, slope of regression line forced through origin, mean bias error (MBE) and root mean square error (RMSE) were used as performance indicators. The first method proposed by Winslow et al. (2001) – Eq. (1) is capable to explain 86% of daily RG variability, with systematic error represented by MBE equaling to 0.19 MJ.m–2.day-1 and random error indicated by RMSE reaching up to 3.09. The second method published by Thornton and Running (1999)-Eq. (2) was found to be in almost all parameters inferior to the Eq. (1) and thus the Eq. (1) is recommended to be used in the Central European region (up to 600 m above the sea level). This method might be recommended for stations where neither measured RG or sunshine duration hours exist. However, one should take into consideration that relative MBE and RMSE are in some months higher than 10% and 30% respectively, which may compromise results of subsequent calculations made with use of estimated solar radiation data and alter the order of the method suitability.

Empirical‐statistical downscaling and error correction of daily precipitation from regional climate models

AbstractAlthough regional climate models (RCMs) are powerful tools for describing regional and even smaller scale climate conditions, they still feature severe systematic errors. In order to provide optimized climate scenarios for climate change impact research, this study merges linear and nonlinear empirical‐statistical downscaling techniques with bias correction methods and investigates their ability for reducing RCM error characteristics. An ensemble of seven empirical‐statistical downscaling and error correction methods (DECMs) is applied to post‐process daily precipitation sums of a high‐resolution regional climate hindcast simulation over the Alpine region, their error characteristics are analysed and compared to the raw RCM results.Drastic reductions in error characteristics due to application of DECMs are demonstrated. Direct point‐wise methods like quantile mapping and local intensity scaling as well as indirect spatial methods as nonlinear analogue methods yield systematic improvements in median, variance, frequency, intensity and extremes of daily precipitation. Multiple linear regression methods, even if optimized by predictor selection, transformation and randomization, exhibit significant shortcomings for modelling daily precipitation due to their linear framework. Comparing the well‐performing methods to each other, quantile mapping shows the best performance, particularly at high quantiles, which is advantageous for applications related to extreme precipitation events. The improvements are obtained regardless of season and region, which indicates the potential transferability of these methods to other regions. Copyright © 2010 Royal Meteorological Society

Spatial analysis of trends in extreme precipitation events in high‐resolution climate model results and observations for Germany

A statistical extreme value analysis is applied to very high‐resolution climate model results and observations encompassing the area of Germany. Two control runs representing the current climate, as well as three scenario simulations of the regional climate model REMO, are investigated. The control runs were compared against high‐resolution observations. The analysis is divided into two main parts: first trends in extreme quantiles of daily precipitation totals are estimated in a station‐by‐station analysis. In the second part, the spatial characteristics of the estimated trends in heavy rainfall are investigated over the area of Germany by fitting a parametric geostatistical model to these trends. The rule of thumb of estimating trends in extreme quantiles of heavy precipitation based on the Clausius‐Clapeyron relation, about 6.5% per 1°C temperature increase, has been roughly confirmed for Germany by our study with respect to the observations, but the climate model computes weaker trends. In the control simulations, the climate model tends to underestimate trends in heavy rainfall compared to observations. In the scenario simulations, positive trends prevail (as in the observations). They are, however, relatively small when set in relation to the uncertainties. The trends become significantly positive to a larger spatial extent only in the A2 scenario simulation. The estimated shape of the extreme value distributions does not change significantly in the scenario simulations compared to the climate model control runs. The parameter estimates for the geostatistical model for the trends in extreme quantiles of daily precipitation sums are rather uncertain. The most striking feature of the analysis is a reduction of the spatial variance of the trends over the considered area of Germany in the scenario simulations compared to observations and, in particular, the climate model control runs.

Statistical Downscaling of Extreme Precipitation Events Using Censored Quantile Regression

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.