Grzegorz J. Ciach

Bio: Grzegorz J. Ciach is an academic researcher from University of Iowa. The author has contributed to research in topics: Radar & Spatial dependence. The author has an hindex of 19, co-authored 31 publications receiving 2245 citations. Previous affiliations of Grzegorz J. Ciach include University of Oklahoma & University of Louisiana at Lafayette.

Local Random Errors in Tipping-Bucket Rain Gauge Measurements

Abstract: This study presents empirical analysis of the errors in tipping-bucket rain gauges that manifest themselves as random differences between closely collocated instruments. It is based on a substantial data sample from 15 collocated rain gauges. The errors are shown to be considerable and highly dependent on rainfall intensity and timescale. These dependencies are estimated using nonparametric regression. Strong dependence of the errors on the data collecting and processing strategy is also demonstrated. An analytical model and estimates of its coefficients are provided to concisely quantify the results in different scenarios. Finally, possible improvements of the accuracy and reliability of the surface rainfall measurements are discussed.

Product-Error-Driven Uncertainty Model for Probabilistic Quantitative Precipitation Estimation with NEXRAD Data

Abstract: Although it is broadly acknowledged that the radar-rainfall (RR) estimates based on the U.S. national network of Weather Surveillance Radar-1988 Doppler (WSR-88D) stations contain a high degree of uncertainty, no methods currently exist to inform users about its quantitative characteristics. The most comprehensive characterization of this uncertainty can be achieved by delivering the products in a probabilistic rather than the traditional deterministic form. The authors are developing a methodology for probabilistic quantitative precipitation estimation (PQPE) based on weather radar data. In this study, they present the central element of this methodology: an empirically based error structure model for the RR products. The authors apply a product-error-driven (PED) approach to obtain a realistic uncertainty model. It is based on the analyses of six years of data from the Oklahoma City, Oklahoma, WSR-88D radar (KTLX) processed with the Precipitation Processing System algorithm of the NEXRAD system...

An analysis of small-scale rainfall variability in different climatic regimes

Abstract: Statistical characteristics of spatial variability in rain fields at the scales below 5 km2 are discussed based on experimental data from five locations worldwide. The work is motivated by the practical needs of remote sensing applications using radar and satellite technologies, and by the scientific needs of rainfall modelling. Two types of rain-field characteristics are described: spatial correlations and conditional probabilities of occurrence. The accumulation intervals range from 5 min to 1 h. The analysed data samples were collected in Brazil, Florida, Iowa, Oklahoma, and on Guam.

The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present)

Abstract: The Global Precipitation Climatology Project (GPCP) Version 2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5 degrees x 2.5 degrees latitude-longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The data set is extended back into the premicrowave era (before 1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the raingauge analysis. This monthly analysis is the foundation for the GPCP suite of products including those at finer temporal resolution, satellite estimate, and error estimates for each field. The 23-year GPCP climatology is characterized, along with time and space variations of precipitation.

Future changes to the intensity and frequency of short‐duration extreme rainfall

Abstract: Evidence that extreme rainfall intensity is increasing at the global scale has strengthened considerably in recent years Research now indicates that the greatest increases are likely to occur in short-duration storms lasting less than a day, potentially leading to an increase in the magnitude and frequency of flash floods This review examines the evidence for subdaily extreme rainfall intensification due to anthropogenic climate change and describes our current physical understanding of the association between subdaily extreme rainfall intensity and atmospheric temperature We also examine the nature, quality, and quantity of information needed to allow society to adapt successfully to predicted future changes, and discuss the roles of observational and modeling studies in helping us to better understand the physical processes that can influence subdaily extreme rainfall characteristics We conclude by describing the types of research required to produce a more thorough understanding of the relationships between local-scale thermodynamic effects, large-scale atmospheric circulation, and subdaily extreme rainfall intensity

MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

Abstract: . Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979–2015 with a 3-hourly temporal and 0.25° spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite- and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44–0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments ( http://www.gloh2o.org .