Rain Rate Estimation Research Articles

Evaluation of Flash-Flood Discharge Forecasts in Complex Terrain Using Precipitation

Operational prediction of flash floods produced by thunderstorm (convective) precipitation in mountainous areas requires accurate estimates or predictions of the precipitation distribution in space and time. The details of the spatial distribution are especially critical in complex terrain because the watersheds are generally small in size, and small position errors in the forecast or observed placement of the precipitation can distribute the rain over the wrong watershed. In addition to the need for good precipitation estimates and predictions, accurate flood prediction requires a surface-hydrologic model that is capable of predicting stream or river discharge based on the precipitation-rate input data. Different techniques for the estimation and prediction of convective precipitation will be applied to the Buffalo Creek, Colorado flash flood of July 1996, where over 75 mm of rain from a thunderstorm fell on the watershed in less than 1 h. The hydrologic impact of the precipitation was exacerbated by the fact that a significant fraction of the watershed experienced a wildfire approximately two months prior to the rain event. Precipitation estimates from the National Weather Service's operational Weather Surveillance Radar–Doppler 1988 and the National Center for Atmospheric Research S-band, research, dual-polarization radar, colocated to the east of Denver, are compared. In addition, very short range forecasts from a convection-resolving dynamic model, which is initialized variationally using the radar reflectivity and Doppler winds, are compared with forecasts from an automated-algorithmic forecast system that also employs the radar data. The radar estimates of rain rate, and the two forecasting systems that employ the radar data, have degraded accuracy by virtue of the fact that they are applied in complex terrain. Nevertheless, the radar data and forecasts from the dynamic model and the automated algorithm could be operationally useful for input to surface-hydrologic models employed for flood warning. Precipitation data provided by these various techniques at short time scales and at fine spatial resolutions are employed as detailed input to a distributed-parameter hydrologic model for flash-flood prediction and analysis. With the radar-based precipitation estimates employed as input, the simulated flood discharge was similar to that observed. The dynamic-model precipitation forecast showed the most promise in providing a significant discharge-forecast lead time. The algorithmic system's precipitation forecast did not demonstrate as much skill, but the associated discharge forecast would still have been sufficient to have provided an alert of impending flood danger.

Drop Size Distributions Measured by a 2D Video Disdrometer: Comparison with Dual-Polarization Radar Data

An analysis of drop size distributions (DSDs) measured in four very different precipitation regimes is presented and is compared with polarimetric radar measurements. The DSDs are measured by a 2D video disdrometer, which is designed to measure drop size, shape, and fall speed with unprecedented accuracy. The observations indicate that significant DSD variability exists not only from one event to the next, but also within each system. Also, despite having vastly different storm structures and maximum rain rates, large raindrops with diameters greater than 5 mm occurred with each system. By comparing the occurrence of large drops with rainfall intensity, the authors find that the largest median diameters are not always associated with the heaviest rainfall, but are sometimes located either in advance of convective cores or, occasionally, in stratiform regions where rainfall rates are relatively low. Disdrometer and polarimetric radar measurements of radar reflectivity Z, differential reflectivity ZDR, specific differential phase KDP, and R(Z) and R(KDP) rain-rate estimators are compared in detail. Overall agreement is good, but it is found that both R(Z) and R(KDP) underestimate rain rate when the DSD is dominated by small drops and overestimate rain rate when the DSD is dominated by large drops. The results indicate that a classification of different rain types (associated with different DSDs) should be an essential part of polarimetric rainfall estimation. Furthermore, observations suggest that ZDR is a key parameter for making such a distinction. Last, the authors compute and compare maximum and average of gamma shape, slope, and intercept parameters for all four precipitation events. Potential measurement errors with the 2D video disdrometer are also discussed.

An ERS-1 synthetic aperture radar image of a tropical squall line compared with weather radar data

A radar image acquired by the C-band synthetic aperture radar (SAR) aboard the European Remote Sensing satellite ERS-2 over the coastal waters south of Singapore showing radar signatures of a strong tropical squall line ("Sumatra Squall") is compared with coincident and collocated weather radar data. Squall line features such as the gust front, areas of updraft convergence, and rain areas are identified. Possible attenuation effects from the rain drops in the atmosphere under very heavy rain (rain rate >100 mm/h) is suggested. In addition, the possibility of extracting the associated geophysical parameters, i.e., rain rate and wind speed from SAR imagery is investigated. The rain rate is estimated from the attenuation signature in the SAR image. Comparison between the estimated rain rate and weather radar rain rate shows consistency. Wind speed associated with the squall line is estimated based on the CMOD4 wind scatterometer model. The estimated wind speed pattern appears to be in agreement with the observed squall line structure. Possible errors in the wind estimation due to effects of rain are suggested.

Statistical Characterization of Path Attenuation of Radar Signals at C Band

Abstract The use of C band in radar meteorology offers clear advantages as far as sensitivity and cost are concerned. On the other hand, the interpretation of the measurements in terms of meteorological quantities is more difficult because of the presence of propagation effects that are not negligible. In this work simulations are used to investigate the impact of the propagation effects on the performance of a C-band dual-polarization meteorological radar; in particular, path attenuation (both total and differential) on rain-rate estimation will be addressed. The simulations are carried out on realistic (horizontal) maps of precipitation, obtained from actual radar measurements, performed at S band (a nonattenuating wavelength) by the meteorological radar located at Spino d’Adda, near Milan, Italy.

Sampling Errors of Tipping-Bucket Rain Gauge Measurements

In this study we investigated sampling-related errors of tipping-bucket (TB) rain gauge measurements, focusing on the gauge’s ability to represent the small-scale rainfall temporal variability. We employed a simple TB simulator that used ultra-high-resolution measurements from an experimental optical rain gauge. The simulated observations were used to provide TB rain rate estimates on time scales as low as one minute. The simulation results showed that the TB estimates suffer from significant errors if based on time scales less than 10 to 15 minutes. We provide the approximate formulas used to characterize the TB sampling errors at several time scales. Our results show the importance of using fine resolution of both the sampling time interval and the bucket size so that the TB rain rate estimates have minimum levels of uncertainty.

A method for estimating rain rate and drop size distribution from polarimetric radar measurements

Polarimetric radar measurements are sensitive to the size, shape and orientation of raindrops and provide information about drop size distribution (DSD), canting angle distribution and rain rate. The authors propose and demonstrate a method for retrieving DSD parameters for calculating rain rate and the characteristic particle size. The DSD is assumed to be a gamma distribution and the governing parameters are retrieved from radar measurements: reflectivity (Z/sub HH/), differential reflectivity (ZDR), and a constrained relation between the shape (CL) and slope (/spl Lambda/) parameters derived from video disdrometer observations. The estimated rain rate is compared with that obtained from more traditional methods and the calculated characteristic size is compared with the measured values. The calculated K/sub DP/ based on the retrieved Gamma DSD is also compared with measurements. The proposed method shows improvement over the existing models and techniques because it can retrieve all three parameters of the gamma distribution. For maintaining the continuity of earlier published results, raindrop shape is assumed to be equilibrium.

A combined microwave/infrared rain rate algorithm

A combined Microwave/Infrared Rain Rate Algorithm (MIRRA) is presented. Historically, infrared algorithms have benefited from excellent temporal sampling, but the relationship between cloud top temperature and surface rain rate is very indirect. Alternatively, passive microwave algorithms are typically more physically direct and accurate, yet the associated sensors do not provide favourable temporal sampling for daily and monthly rainfall amounts. MIRRA is an attempt to utilize the strengths of these two broad approaches to rain rate measurement from space. The algorithm has been tested and developed using data from the TOGA-COARE campaign, with shipboard radar rain rate estimates used as truth. Results indicate enhanced performance in bias, correlation and rms error for MIRRA compared with other infrared and combined algorithms at the instantaneous scale, while retaining the good performance of geostationary algorithms at daily and monthly scales.

Improved rainfall estimates in convective storms using polarisation diversity radar

Abstract. Errors arise when using conventional radar reflectivity, Z, to estimate rainfall rate, R, and these can be particularly severe during severe convective storms; the very events when accurate estimates are needed so that action can be taken to mitigate the effects of flooding. Concentration is on three problems associated with heavy rainfall: hail, attenuation and absolute calibration of the radar, and consider how polarisation radar parameters, differential reflectivity, ZDR, and specific differential phase shift KDP, might lead to their alleviation. It is essential to consider the fundamental limits to the accuracy with which these parameters can be estimated. If ZDR can be measured to an accuracy of 0.2 dB, then it provides a measure of mean raindrop shape which is sufficiently precise to improve rainrate estimates. This can be achieved at S-band (10 cm), but seems very difficult for operational C-band (5 cm) radars; differential attenuation by the heavy rain introduces a negative bias into ZDR which increases with range. However, the magnitude of this bias at C-band can then be used to correct for the total attenuation of Z. Differential phase, KDP has the advantage that it is a phase measurement and so is unaffected by attenuation. It only responds to the rainfall and is unaffected by the hail, but KDP is a noisy parameter and is only useful for heavy rainfall above 30 – 60 mm hr-1. Fortuitously, KDP and ZDR are not independent and one use of KDP and ZDR may well be to exploit this redundancy to identify rain areas as opposed to hail, and in rainfall to use the redundancy to provide an automatic calibration of the absolute reflectivity, Z, to 0.5 dB (12%). Finally, the noisy character of both ZDR and KDP together with the low level of the co-polar correlation coefficient provide the first reliable means of detecting and removing anomalous propagation which is a major operational problem for all weather radars. Keywords: polarisation radar, rainfall calibration, attenuation, hail, anomalous propagation

Use of the Surface Reference Technique for Path Attenuation Estimates from the TRMM Precipitation Radar

Abstract Estimates of rain rate from the precipitation radar (PR) aboard the Tropical Rainfall Measuring Mission (TRMM) satellite require a means by which the radar signal attenuation can be corrected. One of the methods available is the surface reference technique in which the radar surface return in rain-free areas is used as a reference against which the path-integrated attenuation is obtained. Despite the simplicity of the basic concept, an assessment of the reliability of the technique is difficult because the statistical properties of the surface return depend not only on surface type (land/ocean) and incidence angle, but on the detailed nature of the surface scattering. In this paper, a formulation of the technique and a description of several surface reference datasets that are used in the operational algorithm are presented. Applications of the method to measurements from the PR suggest that it performs relatively well over the ocean in moderate to heavy rains. An indication of the reliability of...

Evaluation of PERSIANN System Satellite–Based Estimates of Tropical Rainfall

Abstract PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall from geosynchronous satellite longwave infared imagery (GOES–IR) at a resolution of 0.25° × 0.25° every half–hour. The accuracy of the rainfall product is improved by adaptively adjusting the network parameters using the instantaneous rain–rate estimates from the Tropical Rainfall Measurement Mission (TRMM) microwave imager (TMI product 2A12), and the random errors are further reduced by accumulation to a resolution of 1° × 1° daily. The authors' current GOES–IR–TRMM TMI based product, named PERSIANN–GT, was evaluated over the region 30°S–30°N, 90°E–30°W, which includes the tropical Pacific Ocean and parts of Asia, Australia, and the Americas. The resulting rain–rate estimates agree well with the National Climatic Data Center radar–gauge composite data over Florida and Texas (correlation coefficient r > 0.7). The product al...

Sensitivity Analysis of Polarimetric Variables at a 5-cm Wavelength in Rain

Abstract Sensitivity of 5-cm-wavelength polarimetric radar measurements to raindrop size and distribution is investigated. At this wavelength, resonance occurs for sizes larger than about 5 mm, and therefore several polarimetric variables exhibit nonmonotone dependence on the drop diameter. The resonance effect is examined for temperatures of 0°, 10°, 20°, and 30°C. Through use of truncated gamma distributions (parameterized as a function of rain rate from the Maritime Continent Thunderstorm Experiment), and truncated Marshall–Palmer distributions, the influence of the upper tail of the drop size distribution is brought out. This poorly documented region of sizes can have a significant effect on some polarimetric variables. These simulations are extended further to include estimates of rain rate from the reflectivity factor Z, the specific differential phase KDP, the combined use of KDP and differential reflectivity ZDR, and the combined use of Z and ZDR.

Rainfall and Radiative Heating Rates from TOGA COARE Atmospheric Budgets

Abstract Atmospheric heat and moisture budgets are used to determine rainfall and radiative heating rates over the western Pacific warm pool during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE). Results are compared to independent estimates of these quantities from the other sources. Using the COARE bulk flux algorithm to estimate surface evaporation over the intensive flux array (IFA), the IFA moisture budget-derived average rainfall for the 120-day intensive observing period (IOP) is 8.2 mm day−1. This value agrees closely with recent estimates from satellites and the ocean salinity budget. For a smaller area within the IFA containing the rain-mapping domain of the TOGA and Massachusetts Institute of Technology 5-cm radars, the atmospheric budget for the 101-day radar deployment yields 6.8 mm day−1, slightly greater than the independent radar rain rate estimate of 5.4 mm day−1. Comparison of budget-derived rainfall with National Centers for Environmental ...

Prediction of a Flash Flood in Complex Terrain. Part I: A Comparison of Rainfall Estimates from Radar, and Very Short Range Rainfall Simulations from a Dynamic Model and an Automated Algorithmic System

Operational prediction of flash floods caused by convective rainfall in mountainous areas requires accurate estimates or predictions of the rainfall distribution in space and time. The details of the spatial distribution are especially critical in complex terrain because the watersheds generally are small in size, and position errors in the placement of the rainfall can distribute the rain over the wrong watershed. In addition to the need for good rainfall estimates, accurate flood prediction requires a surface-hydrologic model that is capable of predicting stream or river discharge based on the rainfall-rate input data. In part 1 of this study, different techniques for the estimation and prediction of convective rainfall are applied to the Buffalo Creek, Colorado, flash flood of July 1996, during which over 75 mm of rain from a thunderstorm fell on the watershed in less than 1 h. The hydrologic impact of the rainfall was exacerbated by the fact that a considerable fraction of the watershed experienced a wildfire approximately two months prior to the rain event. Precipitation estimates from the National Weather Service Weather Surveillance Radar-1988 Doppler and the National Center for Atmospheric Research S-band, dual-polarization radar, collocated east of Denver, Colorado, were compared. Very short range simulations from a convection-resolving dynamic model that was initialized variationally using the radar reflectivity and Doppler winds were compared with simulations from an automated algorithmic forecast system that also employs the radar data. The radar estimates of rain rate and the two forecasting systems that employ the radar data have degraded accuracy by virtue of the fact that they are applied in complex terrain. Nevertheless, the dynamic model and automated algorithms both produce simulations that could be useful operationally for input to surface-hydrologic models employed for flood warning. Part 2 of this study, reported in a companion paper, describes experiments in which these radar-based precipitation estimates and dynamic model‐ and automated algorithm‐based precipitation simulations are used as input to a surface-hydrologic model for simulation of the stream discharge associated with the flood.

Assimilation of radar precipitation data in NWP Models — a review

The COST 717 Action has a working group (No 3) on ‘Using Radar Information for Assimilation into Atmospheric Models’. As an initial part of its plan, the group is reviewing previous attempts to assimilate radar data. The main techniques used to assimilate precipitation data — physical initialization, latent heat nudging and variational assimilation are described. Most previous studies have worked with surface rain rate estimates, but attempts to assimilate reflectivity directly are also covered.

Recent developments in assimilation of radar data at the Met. Office

The Met. Office mesoscale model assimilates surface rain rate estimates from the UK weather radar network. Plans are described to improve the radar assimilation by use of a physically based radar quality estimate, which takes account of lower accuracy in derived surface rain rate when the beam is above the freezing level. Work is in progress to extend the data coverage by integration of radars from countries neighbouring the UK to the south. Model impact trials of these changes are scheduled for summer 2000.

Mountain reference technique: Use of mountain returns to calibrate weather radars operating at attenuating wavelengths

The Mountain Reference Technique (MRT) was proposed as a means to perform a self‐calibration of a weather radar system operating at an attenuating wavelength in a mountainous environment. Two convective rain events observed during the Grenoble 97–98 Experiment are selected here for an illustration and a further verification of the method at X band: the June 16, 1997, event is a medium event with maximum path‐integrated attenuations (PIAs) of about 15 dB over a 9‐km path, while the July 3, 1998, event is quite extraordinary with (1) a maximum PIA of 50 dB over the same distance and (2) the temporary presence of hail. An improved scheme is proposed for the MRT parameter estimation procedure with a more satisfactory treatment of such high‐attenuation effects. It is shown that the optimal calibration factors obtained for the two rain events are almost equal to each other, a comforting result with respect to the radar equipment stability during the corresponding 1‐year period. Although the MRT is based on reflectivity and attenuation measurements only, validation of the rain rate retrieval algorithms with respect to rain gage data for the June 16, 1997, rain event showed that this technique is relevant in terms of rain rate estimation. In particular, the MRT‐calibrated Hitschfeld‐Bordan algorithm provides satisfactory results for this medium‐attenuation event. However, the July 3 case clearly demonstrates that this algorithm cannot correct for such high‐attenuation effects because of its inherent instability. For both rain events the Marzoug‐Amayenc algorithm, originally proposed for spaceborne configurations, is found to be stable and efficient in terms of rain rate estimation. These interesting features are counterbalanced by the fact that the algorithm implementation is limited to directions for which PIA measurements are actually available.

A nonlinear multispectral statistical CLEAN-based precipitation parameter-retrieval algorithm

An iterative algorithm incorporating CLEAN deconvolution concepts for precipitation parameter retrieval using passive microwave imagery is presented. The CLEAN algorithm was originally designed to deconvolve single-channel radio astronomy images. In order to use CLEAN to retrieve precipitation parameters from multispectral passive-microwave imagery, extensions of the algorithm to accommodate nonlinear, multispectral, and statistical data mere designed and implemented. The primary advantage of the nonlinear multispectral statistical (NMS) CLEAN retrieval algorithm relative to existing algorithms is the use of high-resolution (high-frequency) imagery to guide the retrievals of precipitation parameters from lower resolution (Low-frequency) imagery. The NMS-CLEAN retrieval algorithm was used to estimate rain rate (RR) and integrated ice content (IIC) using simulated imagery of oceanic convection as would be observed from six channels of the proposed Advanced Microwave-Scanning Radiometer. Both the accuracy and structural detail of the retrieved rain rate were improved relative to the retrievals from a single-step, nonlinear, statistical algorithm. Reduced error and improved spatial resolution of a more minor magnitude was also seen in the integrated ice-content retrievals. This study also showed that spatially-simple storm structures resulted in better performance of the NMS-CLEAN retrieval algorithm.

Oceanic Rain Rate Retrievals Using TRMM Microwave Imager Multi-channel Brightness Temperatures during the 1998 SCSMEX

The purpose of this paper is to retrieve the oceanic rain rate (RR) over waters near Taiwan and Luzon from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) multi-channel brightness temperatures (Tbs). In addition to improving heavy rainfall forecasting by providing quantitative oceanic RR estimates and possible movement direction of Mesoscale Convective Systems (MCSs), another objective of this paper is to overcome the lack of oceanic conventional observations in heavy rainfall forecasting during the Mei-Yu season. Rainfall caused by MCSs during the 1998 South China Sea Monsoon Experiment (SCSMEX) was studied using TMI nine-channel Tbs, the hourly RR from ground rain gauges, and Geostationary Meteorological Satellite (GMS) infrared (IR) Tbs. In this paper, the domain ranges from 110°E to 130°DE and from 15°N to 30°N. Two oceanic RR retrievals taken on 3 June 1998 are shown and examined. The results show that a TMI nine-channel Tbs-RR equation can retrieve the amounts of heavy rainfall caused by the MCSs that often occur during the summer monsoon season in southeastern China, the northern part of the South China Sea, Luzon, and southern Taiwan. The RR retrievals were quantitatively consistent with the rainfall observations made on nearbyislands. The GMS IR Tb 240K isotherms included most of the TMI microwave (MW) Tbs retrieved rainfall region. The heaviest rainfall region wasmore or less included in the area where the IR Tb was colder than 210K. The region had a tendency to be concentrated in the forward part of the MCS in the direction of its movement. Moreover, analyses of the rainfall events that occurred in southern Taiwan during the SCSMEX period show that there was local rainfall enhancement caused by the orographic features of the island.

Hydrologic visibility of weather radars operating in high-mountainous regions: A case study for the toce catchment (Italy) during the mesoscale alpine programme

The use of weather radar systems in mountainous regions comes up against major visibility problems due to (i) the interactions of the electromagnetic waves with the relief (ground clutter and screening effects) and (ii) the vertical structure of the atmospheric reflectivities (bright band, partial beam filling at cloud top). The concept of “hydrologic visibility” was proposed to designate a procedure aimed at pre-determining the quality of rain-rate measurements that a given weather radar system operating with a given scan strategy may obtain over a given hydrological catchment. A case study is presented here to illustrate the procedure for the Toce catchment (1620 km2), a high-elevation mountainous catchment in the Ticino Region, Italy, covered during the Mesoscale Alpine Programme (MAP) with three research and operational weather radars (S-PoUNCAR, Ronsard/CETP, MonteLema/SMI). An inter-comparison of the performance obtained in terms of rain-rate estimation errors with the various radars and various scanning strategies is realized.

A satellite infrared technique for diurnal rainfall variability studies

The development of a satellite infrared technique for estimating convective and stratiform rainfall area and volume and its application in studying the diurnal variability of rainfall in Amazonia are presented. Cloud systems are defined in the technique by the 253 K infrared (IR) temperature isotherm. The convective and stratiform rain areas within these cloud systems are then related to morphologic characteristics of the IR temperature fields. Rainfall rates are assigned to the defined convective and stratiform areas using IR‐ microwave‐derived rain rate probability matching relationships. The training data set consists of three months of collocated IR observations and microwave (MW) rain rate retrievals over a region in the Amazon Basin. Evaluation of instantaneous rain rate estimates over a second independent region in the Amazon showed 25% (−40%) systematic error and 55% (70%) residual random error standard deviation in morning (evening) MW overpasses. The method is used to derive the mean diurnal cycle of rainfall and investigate the relative contribution from its convective and stratiform components. Finally, the technique is applied to study the time evolution of rainfall and the transition from convective to stratiform over selected sites in the Amazon.