scholarly journals A case of severe flood over Albania: a rainfall analysis from a satellite perspective

2006 ◽  
Vol 7 ◽  
pp. 65-72 ◽  
Author(s):  
A. De Luque ◽  
T. Porja ◽  
A. Martín ◽  
J. A. Guijarro ◽  
S. Alonso
Keyword(s):  
Rain Rate ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Convective Rainfall ◽  
Infrared Band ◽  
Rainfall Analysis ◽  
Convective Clouds ◽  
Severe Flood ◽  
Rain Gauge Network ◽  
Cloud Top Temperature

Abstract. This paper presents results of daily rainfall estimates for the flood event in Albania occurred during the end of September 2002 (from the 21 until the 23). Estimated precipitations based on Meteosat-7 data and computed using various techniques, are compared with surface based observations. The two techniques, developed for convective clouds, were employed to screen the Albanian Flood. On one hand a single Infrared band technique known as Auto-estimator and on the other hand a three-channel Convective Rainfall Rate technique known as CRR. Secondly, for both methods, a number of corrections, such as, moisture, cloud growth rate, cloud top temperature gradient, parallax and orographic corrections were, also, performed and tested during the flood case. Preliminary results show that auto-estimator over-measure significantly daily rainfall with respect to the observed while CRR gives much closer rain quantities. The Auto-estimator power law curve was adjusted to the specific conditions using all the available rain rate gauge measurements. Satellite daily rainfall estimated by the two methods, corrected and calibrated were finally evaluated using the Albanian rain gauge network as ground true.

scholarly journals Estimation of convective precipitation: the meteorological radar versus an automatic rain gauge network

2005 ◽  
Vol 2 ◽  
pp. 103-109 ◽  
Author(s):  
M. C. Llasat ◽  
T. Rigo ◽  
M. Ceperuelo ◽  
A. Barrera
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Space Distribution ◽  
Convective Precipitation ◽  
Precipitation Event ◽  
Convective Rainfall ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Meteorological Radar ◽  
Rain Gauge Network

Abstract. The estimation of convective precipitation and its contribution to total precipitation is an important issue both in hydrometeorology and radio links. The greatest part of this kind of precipitation is related with high intensity values that can produce floods and/or damage and disturb radio propagation. This contribution proposes two approaches for the estimation of convective precipitation, using the β parameter that is related with the greater or lesser convective character of the precipitation event, and its time and space distribution throughout the entire series of the samples. The first approach was applied to 126 rain gauges of the Automatic System of Hydrologic Information of the Internal Basins of Catalonia (NE Spain). Data are series of 5-min rain rate, for the period 1996-2002, and a long series of 1-min rain rate starting in 1927. Rainfall events were classified according to this parameter. The second approach involved using information obtained by the meteorological radar located near Barcelona. A modified version of the SCIT method for the 3-D analysis and a combination of different methods for the 2-D analysis were applied. Convective rainfall charts and β charts were reported. Results obtained by the rain gauge network and by the radar were compared. The application of the β parameter to improve the rainfall regionalisation was demonstrated.

Gridded Hourly Precipitation Analysis from High-Density Rain Gauge Network over the Yangtze–Huai Rivers Basin during the 2007 Mei-Yu Season and Comparison with CMORPH

2013 ◽  
Vol 14 (4) ◽  
pp. 1243-1258 ◽  
Author(s):  
Yali Luo ◽  
Weimiao Qian ◽  
Renhe Zhang ◽  
Da-Lin Zhang
Keyword(s):  
Heavy Rainfall ◽  
Rain Gauge ◽  
Rainfall Analysis ◽  
Hourly Precipitation ◽  
Spatial Consistency ◽  
Hourly Rainfall ◽  
Reflectivity Data ◽  
Climate Prediction Center ◽  
Rain Gauge Network

Abstract Heavy rainfall hit the Yangtze–Huai Rivers basin (YHRB) of east China several times during the prolonged 2007 mei-yu season, causing the worst flood since 1954. There has been an urgent need for attaining and processing high-quality, kilometer-scale, hourly rainfall data in order to understand the mei-yu precipitation processes, especially at the mesoβ and smaller scales. In this paper, the authors describe the construction of the 0.07°-resolution gridded hourly rainfall analysis over the YHRB region during the 2007 mei-yu season that is based on surface reports at 555 national and 6572 regional automated weather stations with an average resolution of about 7 km. The gridded hourly analysis is obtained using a modified Cressman-type objective analysis after applying strict quality control, including not only the commonly used internal temporal and spatial consistency and extreme value checks, but also verifications against mosaic radar reflectivity data. This analysis reveals many convectively generated finescale precipitation structures that could not be seen from the national station reports. A comprehensive quantitative assessment ensures the quality of the gridded hourly precipitation data. A comparison of this dataset with the U.S. Climate Prediction Center morphing technique (CMORPH) dataset on the same resolution suggests the dependence of the latter's performance on different rainfall intensity categories, with substantial underestimation of the magnitude and width of the mei-yu rainband as well as the nocturnal and morning peak rainfall amounts, due mainly to its underestimating the occurrences of heavy rainfall (i.e., >10 mm h−1).

scholarly journals Performance evaluation of precipitation prediction skill of NCEP Global Forecasting System (GFS) over Indian region during summer monsoon 2008

MAUSAM ◽  
2021 ◽  
Vol 61 (2) ◽  
pp. 139-154
Author(s):  
V. R. DURAI ◽  
S. K. ROY BHOWMIK ◽  
B. MUKHOPADHYAY
Keyword(s):  
Summer Monsoon ◽  
Model Performance ◽  
Daily Rainfall ◽  
Forecast Model ◽  
Skill Score ◽  
Rain Gauge ◽  
Geographic Region ◽  
Rainfall Analysis ◽  
Rainfall Prediction ◽  
Quantitative Precipitation Estimates

The study provides a concise and synthesized documentation of the current level of skill of the NCEP GFS day-1 to day-5 precipitation forecasts during Indian summer monsoon of 2008, making detailed inter-comparison with daily rainfall analysis from the use of rain gauge observations and satellite (KALPANA-1) derived Quantitative Precipitation Estimates (QPE) obtained from IMD. Model performance is evaluated for day-1 to day-5 forecasts of 24-hr accumulated precipitation in terms of several accuracy and skill measures. Forecast quality and potential value are found to depend strongly on the verification dataset, geographic region and precipitation threshold. Precipitation forecasts of the model, when accumulated over the whole season, reproduce the observed pattern. However, the model predicted rainfall is comparatively higher than the observed rainfall over most parts of the country during the season. The model showed considerable skill in predicting the daily and seasonal mean rainfall over all India and also over four broad homogeneous regions of India. The model bias for rainfall prediction changes from overestimation to underestimation at the threshold of 25 mm/day except for day-1 forecast. Model skill falls dramatically for occurrence rainfall thresholds greater than 10 mm/day. This implies that the model is much better at predicting the occurrence of rainfall than they are at predicting the magnitude and location of the peak values. Various skill score and categorical statistics for the NCEP GFS model rainfall forecast for monsoon 2008 are prepared and discussed.

scholarly journals On Complex Network Construction of Rain Gauge Stations Considering Nonlinearity of Observed Daily Rainfall Data

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1578 ◽  
Author(s):  
Kyunghun Kim ◽  
Hongjun Joo ◽  
Daegun Han ◽  
Soojun Kim ◽  
Taewoo Lee ◽  
...  
Keyword(s):  
Network Analysis ◽  
Mutual Information ◽  
Complex Network ◽  
Pearson Correlation ◽  
Daily Rainfall ◽  
Analysis Data ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Relationship Of ◽  
Rain Gauge Network

Rainfall data is frequently used as input and analysis data in the field of hydrology. To obtain adequate rainfall data, there should be a rain gauge network that can cover the relevant region. Therefore, it is necessary to analyze and evaluate the adequacy of rain gauge networks. Currently, a complex network analysis is frequently used in network analysis and in the hydrology field, Pearson correlation is used as strength of link in constructing networks. However, Pearson correlation is used for analyzing the linear relationship of data. Therefore, it is now suitable for nonlinear hydrological data (such as rainfall and runoff). Thus, a possible solution to this problem is to apply mutual information that can consider nonlinearity of data. The present study used a method of statistical analysis known as the Brock–Dechert–Scheinkman (BDS) statistics to test the nonlinearity of rainfall data from 55 Automated Synoptic Observing System (ASOS) rain gauge stations in South Korea. Analysis results indicated that all rain gauge stations showed nonlinearity in the data. Complex networks of these rain gauge stations were constructed by applying Pearson correlation and mutual information. Then, they were compared by computing their centrality values. Comparing the centrality rankings according to different thresholds for correlation showed that the network based on mutual information yielded consistent results in the rankings, whereas the network, which based on Pearson correlation exhibited much variability in the results. Thus, it was found that using mutual information is appropriate when constructing a complex network utilizing rainfall data with nonlinear characteristics.

scholarly journals Daily Rainfall Detection and Estimation over Land Using Microwave Surface Emissivities

2015 ◽  
Vol 54 (4) ◽  
pp. 880-895 ◽  
Author(s):  
Camille Birman ◽  
Fatima Karbou ◽  
Jean-François Mahfouf
Keyword(s):  
Daily Precipitation ◽  
Daily Rainfall ◽  
Relevant Information ◽  
Radar Data ◽  
Rain Gauge ◽  
Precipitation Rate ◽  
Retrieval Algorithm ◽  
Rainfall Analysis ◽  
Rain Events ◽  
Land Surfaces

AbstractSurface emissivities computed at 89 GHz from AMSU-A, AMSU-B, and SSMI/S instruments are used to detect rain events and to estimate a daily precipitation rate over land surfaces. This new retrieval algorithm, called the emissivity rainfall retrieval (EMIRR) algorithm, is evaluated over France and compared with several other precipitation products. The precipitation detection is performed using temporal changes in daily surface emissivities. A statistical fit, derived from a rainfall analysis product using rain gauge and radar data, is devised to estimate a daily precipitation rate from surface emissivities. Rain retrievals are evaluated over a 1-yr period in 2010 against other precipitation products, including rain gauge measurements. The EMIRR algorithm allows a reasonable detection of rainy events from daily surface emissivities. The number of rainy days and the daily rainfall rates compare well to estimates from other precipitation products. However, the algorithm tends to overestimate low precipitation amounts and to underestimate higher ones, with reduced performances in the presence of snow. Despite such limitations, this new method is very promising and provides a demonstration of the potential use of the 89-GHz surface emissivities to infer relevant information (occurrence and amounts) related to daily precipitation over land surfaces.

Rain rate measurements by a rain gauge network in Singapore

1997 ◽  
Vol 33 (3) ◽  
pp. 240 ◽  
Author(s):  
Jin-Teong Ong ◽  
Chun-Ning Zhu
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Rain Gauge Network

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 Climatology of daily rainfall semi-variance in The Netherlands

2011 ◽  
Vol 15 (1) ◽  
pp. 171-183 ◽  
Author(s):  
C. Z. van de Beek ◽  
H. Leijnse ◽  
P. J. J. F. Torfs ◽  
R. Uijlenhoet
Keyword(s):  
Daily Rainfall ◽  
Rain Gauge ◽  
Time Variability ◽  
Rain Gauges ◽  
Sensing Systems ◽  
Remote Sensing Systems ◽  
One Year ◽  
Rain Gauge Network ◽  
Cosine Functions ◽  
Insight Into

Abstract. Rain gauges can offer high quality rainfall measurements at their locations. Networks of rain gauges can offer better insight into the space-time variability of rainfall, but they tend to be too widely spaced for accurate estimates between points. While remote sensing systems, such as radars and networks of microwave links, can offer good insight in the spatial variability of rainfall they tend to have more problems in identifying the correct rain amounts at the ground. A way to estimate the variability of rainfall between gauge points is to interpolate between them using fitted variograms. If a dense rain gauge network is lacking it is difficult to estimate variograms accurately. In this paper a 30-year dataset of daily rain accumulations gathered at 29 automatic weather stations operated by KNMI (Royal Netherlands Meteorological Institute) and a one-year dataset of 10 gauges in a network with a radius of 5 km around CESAR (Cabauw Experimental Site for Atmospheric Research) are employed to estimate variograms. Fitted variogram parameters are shown to vary according to season, following simple cosine functions. Semi-variances at short ranges during winter and spring tend to be underestimated, but semi-variances during summer and autumn are well predicted.

scholarly journals Dynamical Mechanisms Supporting Extreme Rainfall Accumulations in the Houston “Tax Day” 2016 Flood

2019 ◽  
Vol 148 (1) ◽  
pp. 83-109 ◽  
Author(s):  
Erik R. Nielsen ◽  
Russ S. Schumacher
Keyword(s):  
Rain Rate ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Intense Rainfall ◽  
Convective Rainfall ◽  
Short Term ◽  
Low Level ◽  
Hourly Rainfall ◽  
Scale Characteristics ◽  
Extreme Rain

Abstract This research examines the environmental and storm-scale characteristics of the extreme rainfall and flooding in the Houston, Texas, area on 18 April 2016, known as the “Tax Day” flood. Radar and local mesonet rain gauge observations were used to identify the locations and structures of extreme rain-rate-producing cells, with special attention given to rotating updrafts. To supplement this observation-based analysis, a WRF-ARW simulation of the Tax Day storm in 2016 was examined for the influence of any attendant rotation on both the dynamics and microphysics of the cells producing the most intense short-term (i.e., subhourly to hourly) rainfall accumulations. Results show that the most intense rainfall accumulations in the model analysis, as in the observational analysis, are associated with rotating convective elements. A lowering of the updraft base, enhancement of the low-level vertical velocities, and increased low-level rainwater production is seen in rotating updrafts, compared to those without rotation. These differences are also maintained despite increased hydrometeor loading. The results agree with the findings of previous idealized model simulations that show dynamical accelerations associated with meso-γ-scale rotation can enhance convective rainfall rates.

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