scholarly journals Evaluation of Integrated Nowcasting through Comprehensive Analysis (INCA) precipitation analysis using a dense rain-gauge network in southeastern Austria

2021 ◽  
Vol 25 (8) ◽  
pp. 4335-4356
Author(s):  
Esmail Ghaemi ◽  
Ulrich Foelsche ◽  
Alexander Kann ◽  
Jürgen Fuchsberger
Keyword(s):  
Short Duration ◽  
Precipitation Amount ◽  
Rain Gauge ◽  
Comprehensive Analysis ◽  
Time Shift ◽  
Grid Cells ◽  
Wet Season ◽  
Peak Intensity ◽  
Precipitation Analysis ◽  
Rain Gauge Network

Abstract. An accurate estimate of precipitation is essential to improve the reliability of hydrological models and helps in decision making in agriculture and economy. Merged radar–rain-gauge products provide precipitation estimates at high spatial and temporal resolution. In this study, we assess the ability of the INCA (Integrated Nowcasting through Comprehensive Analysis) precipitation analysis product provided by ZAMG (the Austrian Central Institute for Meteorology and Geodynamics) in detecting and estimating precipitation for 12 years in southeastern Austria. The blended radar–rain-gauge INCA precipitation analyses are evaluated using WegenerNet – a very dense rain-gauge network with about one station per 2 km2 – as “true precipitation”. We analyze annual, seasonal, and extreme precipitation of the 1 km  × 1 km INCA product and its development from 2007 to 2018. From 2007 to 2011, the annual area-mean precipitation in INCA was slightly higher than WegenerNet, except in 2009. However, INCA underestimates precipitation in grid cells farther away from the two ZAMG meteorological stations in the study area (which are used as input for INCA), especially from May to September (“wet season”). From 2012 to 2014, INCA's overestimation of the annual-mean precipitation amount is even higher, with an average of 25 %, but INCA performs better close to the two ZAMG stations. Since new radars were installed during this period, we conclude that this increase in the overestimation is due to new radars' systematic errors. From 2015 onwards, the overestimation is still dominant in most cells but less pronounced than during the second period, with an average of 12.5 %. Regarding precipitation detection, INCA performs better during the wet seasons. Generally, false events in INCA happen less frequently in the cells closer to the ZAMG stations than in other cells. The number of true events, however, is comparably low closer to the ZAMG stations. The difference between INCA and WegenerNet estimates is more noticeable for extremes. We separate individual events using a 1 h minimum inter-event time (MIT) and demonstrate that INCA underestimates the events' peak intensity until 2012 and overestimates this value after mid-2012 in most cases. In general, the precipitation rate and the number of grid cells with precipitation are higher in INCA. Considering four extreme convective short-duration events, there is a time shift in peak intensity detection. The relative differences in the peak intensity in these events can change from approximately −40 % to 40 %. The results show that the INCA analysis product has been improving; nevertheless, the errors and uncertainties of INCA to estimate short-duration convective rainfall events and the peak of extreme events should be considered for future studies. The results of this study can be used for further improvements of INCA products as well as for future hydrological studies in regions with moderately hilly topography and convective dominance in summer.

scholarly journals Evaluation of INCA precipitation analysis using a very dense rain gauge network in southeast Austria

2021 ◽  
Author(s):  
Esmail Ghaemi ◽  
Ulrich Foelsche ◽  
Alexander Kann ◽  
Jürgen Fuchsberger
Keyword(s):  
Precipitation Amount ◽  
Rain Gauge ◽  
Accurate Estimate ◽  
Time Shift ◽  
Grid Cells ◽  
Wet Season ◽  
Event Time ◽  
Peak Intensity ◽  
Precipitation Analysis ◽  
Rain Gauge Network

Abstract. An accurate estimate of precipitation is essential to improve the reliability of hydrological models and helps for decision-making in agriculture and economy. Merged radar–rain-gauge products provide precipitation estimates at high spatial and temporal resolution. In this study, we assess the ability of the INCA (Integrated Nowcasting through Comprehensive Analysis) precipitation analysis product provided by ZAMG (the Austrian Central Institute for Meteorology and Geodynamics) in detecting and estimating precipitation for 12 years in southeast Austria. The blended radar–rain-gauge INCA precipitation analyses are evaluated using WegenerNet – a very dense rain gauge network with about 1 station per 2 km2 – as true precipitation. We analyze annual, seasonal, and extreme precipitation of the 1 km × 1 km INCA product and its development from 2007 to 2018. Based on the results, the performance of INCA can be divided into three different periods. From 2007 to 2011, the annual area-mean precipitation in INCA was slightly higher than WegenerNet, except in 2009. However, INCA underestimates precipitation in grid cells farther away from the two ZAMG meteorological stations in the study area (which are used as input for INCA), especially from May to September (wet season). From 2012 to 2014, INCA's overestimation of the annual-mean precipitation amount is even higher, with an average of 25 %, but INCA performs better close to the two ZAMG stations. From 2015 onwards, the overestimation is still dominant in most cells but less pronounced than during the second period, with an average of 12.5 %. Regarding precipitation detection, INCA performs better during the wet seasons. Generally, false events in INCA happen less frequently in the cells closer to the ZAMG stations than in other cells. The number of true events, however, is comparably low closer to the ZAMG stations. The difference between INCA and WegenerNet estimates is more noticeable for extremes. We separate individual events using a 1-hour minimum inter-event time (MIT) and demonstrate that INCA underestimates the events' peak intensity until 2012 and overestimates this value after mid-2012 in most cases. The overestimation of the peak-intensity is more pronounced during July. In general, the precipitation rate and the number of grid cells with precipitation are higher in INCA. Furthermore, 40 % of the individual events start earlier, and 50 % end later in INCA. Considering four extreme convective short-duration events, there is a time shift in peak intensity detection. The relative differences in the peak intensity in these events can change from approximately −40 % to 40 %. The results of this study can be used for further improvements of INCA products as well as for future hydrological studies in this area.

Comprehensive evaluation of precipitation analyses using a very dense rain gauge network in southeast Austria

2020 ◽  
Author(s):  
Esmail Ghaemi ◽  
Ulrich Foelsche ◽  
Alexander Kann ◽  
Gottfried Kirchengast ◽  
Juergen Fuchsberger
Keyword(s):  
Temporal Resolution ◽  
Comprehensive Evaluation ◽  
Analysis Data ◽  
Radar Data ◽  
Rain Gauge ◽  
Accurate Estimation ◽  
Wet Season ◽  
Rainfall Events ◽  
Rainfall Analysis ◽  
Rain Gauge Network

<p>Precipitation is one of the most important inputs of meteorological and hydrological models and also flood warning systems. Thus, accurate estimation of rainfall is essential for improving the reliability of the models and systems. Although remote sensing (RS) techniques for rainfall estimation (e.g., weather radars and satellite microwave imagers) have improved significantly over the last decades, rain gauges are still more reliable and widely used for this purpose and also for the evaluation of RS estimates. Since the characteristics of a rainfall event can change rapidly in space and time, the accuracy of rain gauge estimation is highly dependent on the spatial and temporal resolution of the gauge network.</p><p>The main aim of this study is to evaluate the ability of the Integrated Nowcasting through Comprehensive Analysis (INCA) of the Central Institute for Meteorology and Geodynamics (ZAMG) to detect and estimate rainfall events. This is done by using 12 years of data from a very dense rain gauge network, the WegenerNet Feldbach region, as a reference, and comparing its data to the INCA analyses. INCA rainfall analysis data are based on a combination of ZAMG ground station data, weather radar data, and high-resolution topographic data. The system provides precipitation rate data with a 1 km spatial grid resolution and 15 minutes temporal resolution. The WegenerNet includes 155 ground stations, almost uniformly spread over a moderate hilly orography area of about 22 km × 16 km.</p><p>After removing outliers and scale WegenerNet data to 1 km, the accuracy of INCA to detect and estimate rainfall events was investigated using 12 years of the dataset. The results show that INCA can detect rainfall events relatively well. It was found that INCA overestimates the rainfall amount between 2012 and 2014, and generally overestimates precipitation for light rainfall events. For heavy rainfall events, however, an underestimation of INCA is prominent in most events. Based on the results, the difference between INCA and WegenerNet estimates is relatively higher during the wet season in the summer half-year (May-September). It is worth pointing out that INCA performs better in detecting and estimating rainfall around the two ZAMG stations located within the study area.</p>

scholarly journals Evaluation of the Research-Version TMPA Rainfall Estimate at Its Finest Spatial and Temporal Scales over the Rome Metropolitan Area

2010 ◽  
Vol 49 (12) ◽  
pp. 2591-2602 ◽  
Author(s):  
Gabriele Villarini
Keyword(s):  
Metropolitan Area ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Survival Functions ◽  
Spatial And Temporal Scales ◽  
Rain Gauges ◽  
Minimum Number ◽  
Precipitation Analysis ◽  
Measures Of Agreement ◽  
Rain Gauge Network

Abstract The focus of this study is the evaluation of the research-version Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) rainfall product at its finest spatial and temporal resolutions (3-hourly and 0.25° × 0.25°) over the Rome, Italy, metropolitan area during the period from October 2008 to January 2009. Accurate ground reference rainfall estimates for two satellite pixels are obtained from a dense rain gauge network (22 rain gauges in one pixel and 16 in the other one). The evaluation is based on examination of time series, scatterplots, and survival functions, as well as measures of agreement and disagreement. The results of this study point to the importance of using the TRMM satellite for rainfall estimation. Suggestions in terms of minimum number of rain gauges required to estimate ground reference rainfall are also provided.

scholarly journals Estimating Rain Rates from Tipping-Bucket Rain Gauge Measurements

2008 ◽  
Vol 25 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Jianxin Wang ◽  
Brad L. Fisher ◽  
David B. Wolff
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Time Shift ◽  
Estimation Errors ◽  
Rate Estimation ◽  
Reflectivity Data ◽  
Rain Rates ◽  
Event Definition

Abstract This paper describes the cubic spline–based operational system for the generation of the Tropical Rainfall Measuring Mission (TRMM) 1-min rain-rate product 2A-56 from tipping-bucket (TB) gauge measurements. A simulated TB gauge from a Joss–Waldvogel disdrometer is employed to evaluate the errors of the TB rain-rate estimation. These errors are very sensitive to the time scale of rain rates. One-minute rain rates suffer substantial errors, especially at low rain rates. When 1-min rain rates are averaged over 4–7-min intervals or longer, the errors dramatically reduce. Estimated lower rain rates are sensitive to the event definition whereas the higher rates are not. The median relative absolute errors are about 22% and 32% for 1-min rain rates higher and lower than 3 mm h−1, respectively. These errors decrease to 5% and 14% when rain rates are used at the 7-min scale. The radar reflectivity–rain-rate distributions drawn from the large amount of 7-min rain rates and radar reflectivity data are mostly insensitive to the event definition. The time shift due to inaccurate clocks can also cause rain-rate estimation errors, which increase with the shifted time length. Finally, some recommendations are proposed for possible improvements of rainfall measurements and rain-rate estimations.

Heavy precipitating events in satellites and rain-gauge products over the Sahel

2021 ◽  
Author(s):  
Sidiki Sanogo ◽  
Philippe Peyrillé ◽  
Romain Roehrig ◽  
Françoise Guichard ◽  
Ousmane Ouedraogo
Keyword(s):  
Burkina Faso ◽  
West African Monsoon ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Reference Dataset ◽  
Extreme Rainfall Events ◽  
Microwave Sensor ◽  
Sahel Region ◽  
Distribution Intensity ◽  
Rain Gauge Network

<p>The Sahel has experienced an increase in the frequency and intensity of extreme rainfall events over the recent decades. These trends are expected to continue in the future. However the properties of these events have so far received little attention. In the present study, we define a heavy precipitating event (HPE) as the occurrence of daily-mean precipitation exceeding a given percentile (e.g., 99<sup>th</sup> and higher) over a 1°x1° pixel and examine their spatial distribution, intensity, seasonality and interannual variability. We take advantage of an original reference dataset based on a rather high-density rain-gauge network over Burkina Faso (142 stations) to evaluate 22 precipitation gridded datasets often used in the literature, based on rain-gauge-only measurements, satellite measurements, or both. Our reference dataset documents the HPEs over Burkina Faso. The 99<sup>th</sup> percentile identifies events greater than 26 mm d<sup>-1</sup> with a ~2.5 mm confidence interval depending on the number of stations within a 1°x1° pixel. The HPEs occur in phase with the West African monsoon annual cycle, more frequently during the monsoon core season and during wet years. The evaluation of the gridded rainfall products reveals that only two of the datasets, namely the rain-gauge-only based products GPCC-DDv1 and REGENv1, are able to properly reproduce all of the HPE features examined in the present work. A subset of the remaining rainfall products also provide satisfying skills over Burkina Faso, but generally only for a few HPE features examined here. In particular, we notice a general better performance for rainfall products that include rain-gauge data in the calibration process, while estimates using microwave sensor measurements are prone to overestimate the HPE intensity. The agreement among the 22 datasets is also assessed over the entire Sahel region. While the meridional gradient in HPE properties is well captured by the good performance subset, the zonal direction exhibit larger inter-products spread. This advocates for the need to continue similar evaluation with the available rain-gauge network available in West Africa, both to enhance the HPE documentation and understanding at the scale of the region and to help improve the rainfall dataset quality.</p>

scholarly journals First Evaluation of the Day-1 IMERG over the Upper Blue Nile Basin

2016 ◽  
Vol 17 (11) ◽  
pp. 2875-2882 ◽  
Author(s):  
Dejene Sahlu ◽  
Efthymios I. Nikolopoulos ◽  
Semu A. Moges ◽  
Emmanouil N. Anagnostou ◽  
Dereje Hailu
Keyword(s):  
Time Scales ◽  
Spatial Scales ◽  
Heavy Precipitation ◽  
Rain Gauge ◽  
Nile Basin ◽  
Wet Season ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Hourly Rainfall ◽  
Upper Blue Nile Basin

Abstract This work presents a first evaluation of the performance of the Integrated Multisatellite Retrievals for GPM (IMERG) precipitation product over the upper Blue Nile basin of Ethiopia. One of the unique features of this study is the availability of hourly rainfall measurements from an experimental rain gauge network in the area. Both the uncalibrated and calibrated versions of IMERG are evaluated, and their performance is contrasted against another high-resolution satellite product, which is the Kalman filter (KF)-based Climate Prediction Center (CPC) morphing technique (CMORPH). The analysis is performed for hourly and daily time scales and at spatial scales that correspond to the nominal resolution of satellite products, which is 0.1° spatial resolution. The period analyzed is focused on a single wet season (May–October 2014). Evaluation is performed using several statistical and categorical error metrics, as well as spatial correlation analysis to assess the ability of satellite products to represent spatial variability of precipitation in the area. Results show that both IMERG products have a better bias ratio and correlation coefficient on both time scales as compared to CMORPH. Comparison statistics show a slight improvement in the skill of detecting rainfall events in IMERG products compared to CMORPH. Results also show a decreasing trend in the detection ability of satellite products for increasing threshold values, highlighting the need to further improve detection during heavy precipitation.

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