scholarly journals Precipitation observation using microwave backhaul links in the alpine and pre-alpine region of Southern Germany

2012 ◽  
Vol 9 (1) ◽  
pp. 741-776 ◽  
Author(s):  
C. Chwala ◽  
A. Gmeiner ◽  
W. Qiu ◽  
S. Hipp ◽  
D. Nienaber ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Alpine Region ◽  
New Method ◽  
Small Data ◽  
Near Surface ◽  
Southern Germany ◽  
Rain Gauges ◽  
Weather Radars ◽  
Data Loggers ◽  
Rain Rates

Abstract. Measuring rain rates over complex terrain is afflicted with large uncertainties because rain gauges are influenced by orography and weather radars are mostly not able to look into mountain valleys. We apply a new method to estimate near surface rain rates exploiting attenuation data from commercial microwave links in the alpine region of Southern Germany. Received signal level (RSL) data is recorded minutely with small data loggers at the towers and then sent to a database server via GSM. Due to the large RSL fluctuations in periods without rain, the determination of attenuation caused by precipitation is not straightforward. To be able to continuously process the RSL data from July 2010 to October 2010, we introduce a new method to detect wet and dry periods using spectral time series analysis. We show the performance and limitations of the method and analyse the derived rain rates compared to rain gauge and weather radar measurements. The resulting correlations differ for different links and reach values of R2 = 0.80 for the link-gauge comparison and R2 = 0.84 for the link-radar comparison.

scholarly journals Precipitation observation using microwave backhaul links in the alpine and pre-alpine region of Southern Germany

2012 ◽  
Vol 16 (8) ◽  
pp. 2647-2661 ◽  
Author(s):  
C. Chwala ◽  
A. Gmeiner ◽  
W. Qiu ◽  
S. Hipp ◽  
D. Nienaber ◽  
...  
Keyword(s):  
Mobile Communications ◽  
Rain Gauge ◽  
Error Rates ◽  
Alpine Region ◽  
New Method ◽  
Small Data ◽  
Near Surface ◽  
Southern Germany ◽  
Weather Radars ◽  
Rain Rates

Abstract. Measuring rain rates over complex terrain is afflicted with large uncertainties, because rain gauges are influenced by orography and weather radars are mostly not able to look into mountain valleys. We apply a new method to estimate near surface rain rates exploiting attenuation data from commercial microwave links in the alpine region of Southern Germany. Received signal level (RSL) data are recorded minutely with small data loggers at the towers and then sent to a database server via GSM (Global System for Mobile Communications). Due to the large RSL fluctuations in periods without rain, the determination of attenuation caused by precipitation is not straightforward. To be able to continuously process the RSL data from July 2010 to October 2010, we introduce a new method to detect wet and dry periods using spectral time series analysis. Its performance and limitations are presented, showing that the mean detection error rates of wet and dry periods can be reduced to 10% for all five links. After, the wet/dry classification rain rates are derived from the RSL and compared to rain gauge and weather radar measurements. The resulting correlations differ for different links and reach values of R2 = 0.81 for the link-gauge comparison and R2 = 0.85 for the link-radar comparison.

scholarly journals Use of Specific Attenuation for Rainfall Measurement at X-Band Radar Wavelengths. Part II: Rainfall Estimates and Comparison with Rain Gauges

2015 ◽  
Vol 16 (2) ◽  
pp. 503-516 ◽  
Author(s):  
Malte Diederich ◽  
Alexander Ryzhkov ◽  
Clemens Simmer ◽  
Pengfei Zhang ◽  
Silke Trömel
Keyword(s):  
Phase Shift ◽  
Rain Gauge ◽  
Differential Phase Shift ◽  
Rain Gauges ◽  
Specific Attenuation ◽  
Weather Radars ◽  
Ground Clutter ◽  
X Band ◽  
Rain Rates ◽  
Rainfall Estimates

Abstract In a series of two papers, rain-rate retrievals based on specific attenuation A at radar X-band wavelength using the R(A) method presented by Ryzhkov et al. are thoroughly investigated. Continuous time series of overlapping measurements from two polarimetric X-band weather radars in Germany during the summers of 2011–13 are used to analyze various aspects of the method, like miscalibration correction, ground clutter contamination, partial beam blockage (PBB), sensitivity to precipitation characteristics, and sensitivity to temperature assumptions in the retrievals. In Part I of the series, the relations inherent to the R(A) method were used to calculate radar reflectivity Z from specific attenuation and it was compared with measured reflectivity to estimate PBB and calibration errors for both radars. In this paper, R(A) rain estimates are compared to R(Z) and R(KDP) retrievals using specific phase shift KDP. PBB and calibration corrections derived in Part I made the R(Z) rainfall estimates almost perfectly consistent. Accumulated over five summer months, rainfall maps showed strong effects of clutter contamination if R(KDP) is used and weaker impact on R(A). These effects could be reduced by processing the phase shift measurements with more resilience toward ground clutter contamination and by substituting problematic R(KDP) or R(A) estimates with R(Z). Hourly and daily accumulations from rain estimators are compared with rain gauge measurements; the results show that R(A) complemented by R(Z) in segments with low total differential phase shift correlates best with gauges and has the lowest bias and RMSE, followed by R(KDP) substituted with R(Z) at rain rates below 8 mm h−1.

scholarly journals Time-dependent <i>Z-R</i> relationships for estimating rainfall fields from radar measurements

2010 ◽  
Vol 10 (1) ◽  
pp. 149-158 ◽  
Author(s):  
L. Alfieri ◽  
P. Claps ◽  
F. Laio
Keyword(s):  
Time Estimation ◽  
Rain Gauge ◽  
Rainfall Rate ◽  
Time Step ◽  
Radar Rainfall ◽  
Rain Gauges ◽  
Weather Radars ◽  
The North ◽  
Real Time Estimation ◽  
Power Law Equation

Abstract. The operational use of weather radars has become a widespread and useful tool for estimating rainfall fields. The radar-gauge adjustment is a commonly adopted technique which allows one to reduce bias and dispersion between radar rainfall estimates and the corresponding ground measurements provided by rain gauges. This paper investigates a new methodology for estimating radar-based rainfall fields by recalibrating at each time step the reflectivity-rainfall rate (Z-R) relationship on the basis of ground measurements provided by a rain gauge network. The power-law equation for converting reflectivity measurements into rainfall rates is readjusted at each time step, by calibrating its parameters using hourly Z-R pairs collected in the proximity of the considered time step. Calibration windows with duration between 1 and 24 h are used for estimating the parameters of the Z-R relationship. A case study pertaining to 19 rainfall events occurred in the north-western Italy is considered, in an area located within 25 km from the radar site, with available measurements of rainfall rate at the ground and radar reflectivity aloft. Results obtained with the proposed method are compared to those of three other literature methods. Applications are described for a posteriori evaluation of rainfall fields and for real-time estimation. Results suggest that the use of a calibration window of 2–5 h yields the best performances, with improvements that reach the 28% of the standard error obtained by using the most accurate fixed (climatological) Z-R relationship.

scholarly journals Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information

2019 ◽  
Vol 20 (5) ◽  
pp. 1015-1026 ◽  
Author(s):  
Nobuyuki Utsumi ◽  
Hyungjun Kim ◽  
F. Joseph Turk ◽  
Ziad. S. Haddad
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Gauge Data ◽  
Surface Rain Rate ◽  
Trmm Precipitation ◽  
Time Lagged ◽  
Rain Rates

Abstract Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.

scholarly journals Precipitation Analysis over the French Alps Using a Variational Approach and Study of Potential Added Value of Ground-Based Radar Observations

2017 ◽  
Vol 18 (5) ◽  
pp. 1425-1451 ◽  
Author(s):  
Camille Birman ◽  
Fatima Karbou ◽  
Jean-François Mahfouf ◽  
Matthieu Lafaysse ◽  
Yves Durand ◽  
...  
Keyword(s):  
Snow Depth ◽  
Positive Impact ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Added Value ◽  
Background Information ◽  
Time Step ◽  
Rain Gauges ◽  
Weather Radars ◽  
Precipitation Estimates

Abstract A one-dimensional variational data assimilation (1DVar) method to retrieve profiles of precipitation in mountainous terrain is described. The method combines observations from the French Alpine region rain gauges and precipitation estimates from weather radars with background information from short-range numerical weather prediction forecasts in an optimal way. The performance of this technique is evaluated using measurements of precipitation and of snow depth during two years (2012/13 and 2013/14). It is shown that the 1DVar model allows an effective assimilation of measurements of different types, including rain gauge and radar-derived precipitation. The use of radar-derived precipitation rates over mountains to force the numerical snowpack model Crocus significantly reduces the bias and standard deviation with respect to independent snow depth observations. The improvement is particularly significant for large rainfall or snowfall events, which are decisive for avalanche hazard forecasting. The use of radar-derived precipitation rates at an hourly time step improves the time series of precipitation analyses and has a positive impact on simulated snow depths.

scholarly journals Evaluation of the High-Resolution CMORPH Satellite Rainfall Product Using Dense Rain Gauge Observations and Radar-Based Estimates

2012 ◽  
Vol 13 (6) ◽  
pp. 1784-1798 ◽  
Author(s):  
Emad Habib ◽  
Alemseged Tamiru Haile ◽  
Yudong Tian ◽  
Robert J. Joyce
Keyword(s):  
Rain Gauge ◽  
Random Errors ◽  
Event Scale ◽  
Entire Study ◽  
Rain Gauges ◽  
Fine Resolution ◽  
Rain Rates ◽  
Climate Prediction Center ◽  
Rain Gauge Network ◽  
Insight Into

Abstract This study focuses on the evaluation of the NOAA–NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based rainfall product at fine space–time resolutions (1 h and 8 km). The evaluation was conducted during a 28-month period from 2004 to 2006 using a high-quality experimental rain gauge network in southern Louisiana, United States. The dense arrangement of rain gauges allowed for multiple gauges to be located within a single CMORPH pixel and provided a relatively reliable approximation of pixel-average surface rainfall. The results suggest that the CMORPH product has high detection skills: the probability of successful detection is ~80% for surface rain rates &gt;2 mm h−1 and probability of false detection &lt;3%. However, significant and alarming missed-rain and false-rain volumes of 21% and 22%, respectively, were reported. The CMORPH product has a negligible bias when assessed for the entire study period. On an event scale it has significant biases that exceed 100%. The fine-resolution CMORPH estimates have high levels of random errors; however, these errors get reduced rapidly when the estimates are aggregated in time or space. To provide insight into future improvements, the study examines the effect of temporal availability of passive microwave rainfall estimates on the product accuracy. The study also investigates the implications of using a radar-based rainfall product as an evaluation surface reference dataset instead of gauge observations. The findings reported in this study guide future enhancements of rainfall products and increase their informed usage in a variety of research and operational applications.

scholarly journals Statistical and neural classifiers in estimating rain rate from weather radar measurements

2007 ◽  
Vol 10 ◽  
pp. 111-115
Author(s):  
C. I. Christodoulou ◽  
S. C. Michaelides
Keyword(s):  
Electromagnetic Radiation ◽  
Rain Rate ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Meteorological Conditions ◽  
Swiss Alps ◽  
Rain Gauges ◽  
Weather Radars ◽  
Radar Measurements

Abstract. Weather radars are used to measure the electromagnetic radiation backscattered by cloud raindrops. Clouds that backscatter more electromagnetic radiation consist of larger droplets of rain and therefore they produce more rain. The idea is to estimate rain rate by using weather radar as an alternative to rain-gauges measuring rainfall on the ground. In an experiment during two days in June and August 1997 over the Italian-Swiss Alps, data from weather radar and surrounding rain-gauges were collected at the same time. The statistical KNN and the neural SOM classifiers were implemented for the classification task using the radar data as input and the rain-gauge measurements as output. The proposed system managed to identify matching pattern waveforms and the rainfall rate on the ground was estimated based on the radar reflectivities with a satisfactory error rate, outperforming the traditional Z/R relationship. It is anticipated that more data, representing a variety of possible meteorological conditions, will lead to improved results. The results in this work show that an estimation of rain rate based on weather radar measurements treated with statistical and neural classifiers is possible.

Statistical postprocessing of heavy precipitation

2020 ◽  
Author(s):  
Reinhold Hess ◽  
Peter Schaumann ◽  
Volker Schmidt
Keyword(s):  
Point Processes ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Rain Gauge ◽  
Observation System ◽  
Small Areas ◽  
Rain Gauges ◽  
Long Time ◽  
Synoptic Observations ◽  
Rain Rates

&lt;p&gt;Heavy precipitation rates of more than 15 mm per hour are captured only about once a year at each rain gauge within Germany. More extreme events are even less frequent. Point by point verifications show that forecasts of heavy precipitation of the ensemble system COSMO-D2-EPS of DWD can be improved by statistical postprocessing. This is done in a MOS approach using long time series of synoptic observations and numerical forecasts that are required in or&amp;#173;der to gather a significant number of heavy precipitation events for reliable statistical model&amp;#173;ling.&lt;/p&gt;&lt;p&gt;Highest precipitation rates of convective events usually realise more likely in the surrounding of rain gauges rather than exactly above their small funnels. Statistical forecasts modelling these point observations usually underestimate maximal rain rates and result in low probabili&amp;#173;ties for the occurrence of heavy precipitation at a given location.&lt;/p&gt;&lt;p&gt;Point processes of stochastic geometry can be used to model area probabilities that provide the probability that precipitation occurs anywhere (at least at one point) within that area. Verifications with gauge adjusted radar data reveal that point probabilities are representative for very small areas, but area probabilities are significantly larger already for areas of 20*20 km&lt;sup&gt;2&lt;/sup&gt;.&lt;/p&gt;&lt;p&gt;The use of radar data as area observation system allows to statistically generate calibrated precipitation forecasts for arbitrary areas. However, the question remains, which size of area is most relevant for the public and most suitable for weather warnings.&lt;/p&gt;

Rainfall monitoring in Sri Lanka employing commercial microwave links

2021 ◽  
Author(s):  
Aart Overeem ◽  
Hidde Leijnse ◽  
Thomas van Leth ◽  
Linda Bogerd ◽  
Jan Priebe ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Land Surface ◽  
Urban Areas ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Cellular Communication ◽  
Dual Frequency ◽  
Middle Income ◽  
Rain Gauges ◽  
Weather Radars

&lt;p&gt;Microwave backhaul links from cellular communication networks provide a valuable &amp;#8220;opportunistic&amp;#8221; source of high-resolution space&amp;#8211;time rainfall information, complementing traditional in situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Over the past decade, a growing community of researchers has, in close collaboration with cellular communication companies, developed retrieval algorithms to convert the raw microwave link signals, stored operationally by their network management systems, to hydrometeorologically useful rainfall estimates. Operational meteorological and hydrological services as well as private consulting firms are showing an increased interest in using this complementary source of rainfall information to improve the products and services they provide to end users from different sectors, from water management and weather prediction to agriculture and traffic control. The greatest potential of these opportunistic environmental sensors lies in those geographical areas over the land surface of the Earth with few rain gauges and no weather radars: often mountainous and urban areas, but especially low- to middle-income regions, which are generally in (sub)tropical climates.&amp;#160;&lt;/p&gt;&lt;p&gt;Here, the open-source R package RAINLINK is employed to retrieve CML rainfall maps covering the majority of Sri Lanka, a middle-income country having a tropical climate. This is performed for a 3.5-month period based on CML data from on average 1140 link paths. CML rainfall maps are compared locally to hourly and daily rain gauge data, as well as to rainfall maps from the Dual-frequency Precipitation Radar on board the Global Precipitation Measurement Core Observatory satellite. The results confirm the potential of CMLs for real-time tropical rainfall monitoring. This holds a promise for, e.g., ground validation of or merging with satellite precipitation products.&lt;/p&gt;

scholarly journals Using High-Speed Photography to Study Undercatch in Tipping-Bucket Rain Gauges*

2014 ◽  
Vol 31 (6) ◽  
pp. 1330-1336 ◽  
Author(s):  
Claude Duchon ◽  
Christopher Fiebrich ◽  
David Grimsley
Keyword(s):  
High Speed ◽  
Rain Rate ◽  
Slow Motion ◽  
Rain Gauge ◽  
The Other ◽  
High Speed Photography ◽  
High Speed Camera ◽  
Rain Gauges ◽  
Linear Correction ◽  
Rain Rates

Abstract To better understand the undercatch process associated with tipping-bucket rain gauges, a high-speed camera normally used in determining the structure of lightning was employed. The photo rate was set at 500 frames per second to observe the tipping of the bucket in a commonly used tipping-bucket rain gauge. The photos showed detail never seen before as the bucket tipped from one side to the other. Two fixed rain rates of 19.9 mm h−1 (0.78 in. h−1) and 175.2 mm h−1 (6.90 in. h−1), the minimum and maximum available, respectively, were used. The data from four tips at each rain rate were examined. The results show that the time from the beginning of a tip to the time the bucket assembly is horizontal—defined as the period during which undercatch occurs—was an average of 0.450 s for the eight cases. The average time for a complete tip was 0.524 s; thus, the vast majority of the time of a tip, 86%, is spent in undercatch mode. Because there was no apparent dependence of these times on rain rate, it should be possible to apply an accurate linear correction for undercatch as a function of rain rate given the time that undercatch occurs during a tip. Over all eight tips, the undercatch was found to be 0.98% for the 19.9 mm h−1 rate and 8.78% for the 175.2 mm h−1 rate. The procedure used to estimate the undercatch is described. Slow motion videos of the tipping of a bucket are available online.

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