Rain Rate Retrieval Research Articles

Assessment of High-Resolution Satellite-Based Rainfall Estimates over the Mediterranean during Heavy Precipitation Events

Abstract Heavy precipitation events (HPE) can incur significant economic losses as well as losses of lives through catastrophic floods. Evidence of increasing heavy precipitation at continental and global scales clearly emphasizes the need to accurately quantify these phenomena. The current study focuses on the error analysis of two of the main quasi-global, high-resolution satellite products [Climate Prediction Center (CPC) morphing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)], using rainfall data derived from high-quality weather radar rainfall estimates as a reference. This analysis is based on seven major flood-inducing HPEs that developed over complex terrain areas in northern Italy (Fella and Sessia regions) and southern France (Cevennes–Vivarais region). The storm cases were categorized as convective or stratiform based on their characteristics, including rainfall intensity, duration, and area coverage. The results indicate that precipitation type has an effect on the algorithm's ability to capture rainfall effectively. Convective storm cases exhibited greater rain rate retrieval errors, while low rain rates in stratiform-type systems are not well captured by the satellite algorithms investigated in this study, thus leading to greater missed rainfall volumes. Overall, CMORPH exhibited better error statistics than PERSIANN for the HPEs of this study. Similarities are also shown in the two satellite products' error characteristics for the HPEs that occurred in the same geographical area.

The 183-WSL fast rain rate retrieval algorithm. Part II: Validation using ground radar measurements

The Water vapor Strong Lines at 183GHz (183-WSL) algorithm is a method for the retrieval of rain rates and precipitation type classification (convective/stratiform). It exploits the water vapor absorption line observations centered at 183.31GHz of the Advanced Microwave Sounding Unit module B (AMSU-B) and of the Microwave Humidity Sounder (MHS) flying on NOAA-15/-17 and NOAA-18-19/MetOp-A satellite series, respectively. The characteristics of this algorithm were described in Part I of this paper together with comparisons against analogous precipitation products. The focus of Part II is the analysis of the performance of the 183-WSL technique based on surface radar measurements. The “ground truth” dataset consists of 2years and 7months of rainfall intensity fields from the NIMROD radar network, which covers North-Western Europe. The investigation of the 183-WSL retrieval performance is based on a twofold approach: 1) the dichotomous statistic is used to evaluate the capabilities of the method to identify rain and no-rain clouds and 2) the accuracy statistic is applied to quantify the errors in the estimation of rain rates.The results reveal that the 183-WSL technique shows good skills in the detection of rain/no-rain areas and in the quantification of rain rate intensities. The categorical analysis shows annual values of the Probability Of Detection (POD), False Alarm Ratio (FAR) and Hanssen–Kuiper discriminant (HK) indices varying in the range 0.80–0.82, 0.33–0.36 and 0.39–0.46, respectively. The RMSE value is 2.8mmh−1 for the whole period despite an overestimation in the retrieved rain rates. Of note is the distribution of the 183-WSL monthly mean rain rate with respect to radar: the seasonal fluctuations of the average rainfalls measured by radar are reproduced by the 183-WSL. However, the retrieval method appears to suffer during winter seasonal conditions especially when the soil is partially frozen and the surface emissivity drastically changes. This is verified by the discrepancy distribution diagrams where the 183-WSL performs better during the warm months, while during the winter time the discrepancies with radar measurements tend to maximum values. The stable behavior of the 183-WSL algorithm is demonstrated over the whole study period by an overall overestimation for rain rate intensities less than 1mmh−1. This threshold is especially crucial in wintertime when the classification of low-intensity precipitation regimes is difficult.

A monostatic microwave transmission experiment for line integrated precipitation and humidity remote sensing

Near-surface water vapor and precipitation are central hydrometeorological observables which are still difficult to quantify accurately above the point scale. Both play an important role in modeling and remote sensing of the hydrologic cycle. We present details on the development of a new microwave transmission experiment that is capable of providing line integrated estimates of both humidity and precipitation near the surface. The system is located at a hydrometeorological test site (TERENO-prealpine) in Southern Germany. Path length is kept short at 660m to minimize the likelihood of different precipitation types and intensities along the path. It uses a monostatic configuration with a combined transmitter/receiver unit and a 70cm trihedral reflector. The transmitter/receiver unit simultaneously operates at 22.235GHz and 34.8GHz with a pulse repetition rate of 25kHz and alternating horizontal and vertical polarization, which enable the analysis of the impact of the changing drop size distribution on the rain rate retrieval. Due to the coherence and the high phase stability of the system, it allows for a sensitive observation of the propagation phase delay. Thereof, time series of line integrated refractivity can be determined. This proxy is then post-processed to absolute humidity and compared to station observations. We present the design of the system and show an analysis of selected periods for both, precipitation and humidity observations. The theoretically expected dependence of attenuation and differential attenuation on the DSD was reproduced with experimental data. A decreased performance was observed when using a fixed A–R power law. Humidity data derived from the phase delay measurement showed good agreement with in situ measurements.

Signal Postprocessing and Reflectivity Calibration of the Atmospheric Radiation Measurement Program 915-MHz Wind Profilers

Abstract The Department of Energy Atmospheric Radiation Measurement (ARM) Program has recently initiated a new research avenue toward a better characterization of the transition from cloud to precipitation. Dual-wavelength techniques applied to millimeter-wavelength radars and a Rayleigh reference have a great potential for rain-rate retrievals directly from dual-wavelength ratio measurements. In this context, the recent reconfiguration of the ARM 915-MHz wind profilers in a vertically pointing mode makes these instruments the ideal candidate for providing the Rayleigh reflectivity/Doppler velocity reference. Prior to any scientific study, the wind profiler data must be carefully quality checked. This work describes the signal postprocessing steps that are essential for the delivery of high-quality reflectivity and mean Doppler velocity products—that is, the estimation of the noise floor from clear-air echoes, the absolute calibration with a collocated disdrometer, the dealiasing of Doppler velocities, and the merging of the different modes of the wind profiler. The improvement added by the proposed postprocessing is confirmed by comparison with a high-quality S-band profiler deployed at the ARM Southern Great Plains site during the Midlatitude Continental Convective Clouds Experiment. With the addition of a vertically pointing mode and with the postprocessing described in this work in place, besides being a key asset for wind research wind profilers observations may therefore become a centerpiece for rain studies in the years to come.

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

Abstract. Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) is a EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) program, designed to deliver satellite products of hydrological interest (precipitation, soil moisture and snow parameters) over the European and Mediterranean region to research and operations users worldwide. Six satellite precipitation algorithms and concomitant precipitation products are the responsibility of various agencies in Italy. Two of these algorithms have been designed for maximum accuracy by restricting their inputs to measurements from conical and cross-track scanning passive microwave (PMW) radiometers mounted on various low Earth orbiting satellites. They have been developed at the Italian National Research Council/Institute of Atmospheric Sciences and Climate in Rome (CNR/ISAC-Rome), and are providing operational retrievals of surface rain rate and its phase properties. Each of these algorithms is physically based, however, the first of these, referred to as the Cloud Dynamics and Radiation Database (CDRD) algorithm, uses a Bayesian-based solution solver, while the second, referred to as the PMW Neural-net Precipitation Retrieval (PNPR) algorithm, uses a neural network-based solution solver. Herein we first provide an overview of the two initial EU research and applications programs that motivated their initial development, EuroTRMM and EURAINSAT (European Satellite Rainfall Analysis and Monitoring at the Geostationary Scale), and the current H-SAF program that provides the framework for their operational use and continued development. We stress the relevance of the CDRD and PNPR algorithms and their precipitation products in helping secure the goals of H-SAF's scientific and operations agenda, the former helpful as a secondary calibration reference to other algorithms in H-SAF's complete mix of algorithms. Descriptions of the algorithms' designs are provided including a few examples of their performance. This aspect of the development of the two algorithms is placed in the context of what we refer to as the TRMM era, which is the era denoting the active and ongoing period of the Tropical Rainfall Measuring Mission (TRMM) that helped inspire their original development. In 2015, the ISAC-Rome precipitation algorithms will undergo a transformation beginning with the upcoming Global Precipitation Measurement (GPM) mission, particularly the GPM Core Satellite technologies. A few years afterward, the first pair of imaging and sounding Meteosat Third Generation (MTG) satellites will be launched, providing additional technological advances. Various of the opportunities presented by the GPM Core and MTG satellites for improving the current CDRD and PNPR precipitation retrieval algorithms, as well as extending their product capability, are discussed.

Performance Evaluation of a New Dual-Polarization Microphysical Algorithm Based on Long-Term X-Band Radar and Disdrometer Observations

AbstractAccurate estimation of precipitation at high spatial and temporal resolution of weather radars is an open problem in hydrometeorological applications. The use of dual polarization gives the advantage of multiparameter measurements using orthogonal polarization states. These measurements carry significant information, useful for estimating rain-path signal attenuation, drop size distribution (DSD), and rainfall rate. This study evaluates a new self-consistent with optimal parameterization attenuation correction and rain microphysics estimation algorithm (named SCOP-ME). Long-term X-band dual-polarization measurements and disdrometer DSD parameter data, acquired in Athens, Greece, have been used to quantitatively and qualitatively compare SCOP-ME retrievals of median volume diameter D0 and intercept parameter NW with two existing rain microphysical estimation algorithms and the SCOP-ME retrievals of rain rate with three available radar rainfall estimation algorithms. Error statistics for rain rate estimation, in terms of relative mean and root-mean-square error and efficiency, show that the SCOP-ME has low relative error if compared to the other three methods, which systematically underestimate rainfall. The SCOP-ME rain microphysics algorithm also shows a lower relative error statistic when compared to the other two microphysical algorithms. However, measurement noise or other signal degradation effects can significantly affect the estimation of the DSD intercept parameter from the three different algorithms used in this study. Rainfall rate estimates with SCOP-ME mostly depend on the median volume diameter, which is estimated much more efficiently than the intercept parameter. Comparisons based on the long-term dataset are relatively insensitive to path-integrated attenuation variability and rainfall rates, providing relatively accurate retrievals of the DSD parameters when compared to the other two algorithms.

Application of lightning to passive microwave convective and stratiform partitioning in passive microwave rainfall retrieval algorithm over land from TRMM

This study analyzes relationships between lightning flash rate, radar reflectivity factor (reflectivity), and passive microwave brightness temperature (Tb) for convective and stratiform precipitation over land using multiyear data from the Tropical Rainfall Measuring Mission (TRMM) satellite. A new convective and stratiform index (CSI (an estimate of convective areal fraction)) for the TRMM Microwave Imager (TMI) is developed from the analysis. Four years of TRMM TMI, Lightning Imaging Sensor (LIS), and Precipitation Radar (PR) data (2002–2005) are colocated and remapped to 0.1 and 0.05 degree grids for analysis. The scientific objective of this study is to understand the relationship between lightning and active and passive microwave precipitation observations and explore ways of using lightning information to enhance the discrimination between convective and stratiform precipitation in TMI rain rate retrieval algorithm. PR provides the reference convective and stratiform classification and is coincident with LIS which reports lightning parameters such as the occurrence (yes or no) and flash rates. Analysis of ∼14 million coincident precipitating TRMM measurements over land (i.e., excluding oceans and coasts) reveals that 6% of rain data have lightning flash rates greater than zero. For all lightning data, 60% have 0–1 fl min−1, 28% have 1–2 fl min−1, and 12% have flash rates greater than 2 fl min−1. Overall, 86.5% (13.5%) of lightning occurred in convective (stratiform) precipitation. In other words, stratiform rainfall is predominant when LIS detects no lightning, and the convective rain probability increases with increasing lightning frequency. For example, 34% of rainfall is convective for low flash rates (0–1 fl/min), whereas the convective probability increases to 99.7% for high flash rates (>=2 fl/min). This study develops a simple method that incorporates lightning into the CSI to test if lightning can help passive microwave (PM) delineate convective and stratiform precipitation. LIS lightning occurrence and flash rates (i.e., no flash, 0–1 fl/min, 1–2 fl/min, and >2 fl/min) are used to preclassify TMI Tbs into four groups of increasing convective probability. Multivariable linear regression is then applied to each group to derive the CSI. Results reveal that lightning information primarily improves the identification of highly convective rainfall by correctly shifting microwave observations previously identified as moderate to highly convective. Alternatively, the absence of lightning also helps PM to identify likely stratiform. Overall, including lightning information results in a decrease of bias error of 6% and a small increase in RMS error of 4.5% on the entire range of rainfall rates.

The relationship between surface rainrate and water paths and its implications to satellite rainrate retrieval

Since brightness temperature measured by a passive microwave radiometer reflects the integrated effects of water paths, not surface rainrate, the accuracy of surface rainrate retrieval based on brightness temperatures relies heavily on the natural correlation between surface rainrate and water paths. In this study, we investigate how the water paths are related to surface rainrates using satellite data. It is found that corresponding to a similar surface rainrate ice water path is larger over land than over ocean. In addition, on average, the correlation (R2) between surface rainrate and ice water path over land (0.36) is also larger than over ocean (0.25). Over ocean, the correlation between surface rainrate and ice water path is greatly reduced where shallow convections exist. The results also show that given a similar surface rainrate, greater values of total water path occur over arid regions than over other land areas while the correlation between surface rainrate and total water path over such regions is smaller. Furthermore, given a similar surface rainrate, over ocean, larger values of liquid water path are observed over the intertropical convergence zone and maritime regions while smaller values appear over regions dominated by shallow convections. The explanation of the above differences in correlations is attempted using the seasonal variability of water paths and the rainfall systems' vertical structures. It is expected that the low correlation between surface rainrate and water paths will be a major obstacle for surface rainrate retrieval using microwave observations. In addition, surface rainrate always possesses a better relationship to liquid water path over ocean (or total water path over land) than to ice water path. Therefore, for surface rainrate retrievals, channels or channel combinations that can capture liquid water signal over ocean (or total water signal over land) are preferable to those solely containing ice water information.

Estimating the Accuracy of Polarimetric Radar–Based Retrievals of Drop-Size Distribution Parameters and Rain Rate: An Application of Error Variance Separation Using Radar-Derived Spatial Correlations

Abstract The accuracy of retrieving the two drop size distribution (DSD) parameters, median volume diameter (D0), and normalized intercept parameter (NW), as well as rain rate (R), from polarimetric C-band radar data obtained during a cool-season, long-duration precipitation event in Huntsville, Alabama, is examined. The radar was operated in a special “near-dwelling” mode over two video disdrometers (2DVD) located 15 km away. The polarimetric radar–based retrieval algorithms for the DSD parameters and rain rate were obtained from simulations using the 2DVD measurements of the DSD. A unique feature of this paper is the radar-based estimation of the spatial correlation functions of the two DSD parameters and rain rate that are used to estimate the “point-to-area” variance. A detailed error variance separation is performed, including the aforementioned point-to-area variance, along with variance components due to the retrieval algorithm error, radar measurement error, and disdrometer sampling error. The spatial decorrelation distance was found to be smallest for the R (4.5 km) and largest for D0 (8.24 km). For log10(NW), it was 7.22 km. The proportion of the variance of the difference between radar-based estimates and 2DVD measurements that could be explained by the aforementioned errors was 100%, 57%, and 73% for D0, log10(NW), and R, respectively. The overall accuracy of the radar-based retrievals for the particular precipitation event quantified in terms of the fractional standard deviation were estimated to be 6.8%, 6%, and 21% for D0, log10(NW), and R, respectively. The normalized bias was <1%. These correspond to time resolution of ~3 min and spatial resolution of ~1.5 km.

On the Effect of Non-Raining Parameters in Retrieval of Surface Rain Rate Using TRMM PR and TMI Measurements

Microwave radiation with the inherent advantage of its ability to partially penetrate clouds is ideally suited for remote measurements of precipitation, especially over the oceanic regions. The retrieval problem is of great practical interest, as precipitation over the oceans has to be necessarily remotely sensed. More importantly, precipitation is also a crucial input in many weather and climate models. A downward looking space borne radiometer such as the TRMM's Microwave Imager (TMI), however, does not sense the surface rain fall directly. Rather, it measures the upwelling radiation coming from the top-of-atmosphere which depends on the total quantities of the parameters that can affect the radiation in the chosen frequency range. In addition to precipitation, the attenuation and augmentation of total cloud content and integrated precipitable water content by absorption and emission, respectively, affect the microwave brightness temperatures in various frequencies. In the present work, a systematic study has been conducted to investigate the effect of total cloud and precipitable water contents on surface rainrate retrievals from the TMI measured brightness temperatures (BT). While a Bayesian framework is used to assimilate radar reflectivities into hydrometeor structures, a neural network is used to correlate rain and cloud parameters with brightness temperatures. The correlation between the TMI brightness temperatures and the TRMM's Precipitation Radar (TRMM-PR) is used as the benchmark for comparison. A community developed software meso-scale Weather Research and Forecast (WRF) is used to simulate the cloud and precipitable water content, along with the surface rainfall rate for several rain events in the past. Four cases are considered: (a) TRMM PR's near surface rain rate is correlated with TMI brightness temperatures directly; (b) the total cloud and precipitable water contents along with surface rainfall simulated using WRF are correlated with TMI BTs; (c) similar to case (b) with near surface rainfall taken from TRMM PR measurements; (d) total cloud and precipitable water contents and the surface rain rate are corrected with PR vertical reflectivity profile in a Bayesian framework. The freely available QuickBeam software has been used for simulation of reflectivities at the TRMM PR frequency. The corrected data are then correlated with TMI BTs. Results show that surface rain fall retrievals can be radically improved by using TRMM PR vertical rain corrected total cloud and precipitable water content.

Empirical Test of Theoretically Based Correction for Path Integrated Attenuation in Simulated Spaceborne Precipitation Radar Observations

Data from the TRMM precipitation radar is providing unprecedented information on the 3D structure of precipitation systems and estimates of precipitation rates over the oceans and land of the global tropics and subtropics. Algorithms for estimating precipitation rates from observations of apparent radar reflectivity depend on procedures for correcting for attenuation, especially in regions where intense deep convection occurs. The well-known problem of non-uniform beam filling is a source of error in the estimates, caused by unresolved horizontal variability in highly correlated characteristics of precipitation, such as specific attenuation, rain rate, and the effective radar reflectivity factor, that are fundamentally related to the size distribution of hydrometeors. This paper presents an empirical test of a theoretically based procedure for correcting for attenuation by means of a simulation study. Data for simulating spaceborne radar observations were obtained from a ground based scanning radar in Okinawa during a field experiment in June 2004. The correction procedure, reviewed briefly here, has been developed by formulating and analytically solving a statistically based model of non-uniform beam filling. The empirical test shows that the correction has the potential to improve retrievals of rain rate in intense convection, provided that reasonable estimates of a governing parameter can be obtained from the satellite data.

Regional Variability within Global-Scale Relations between Passive Microwave Signatures and Raining Clouds over the Tropical Oceans

AbstractEmpirical orthogonal function (EOF) analysis of radiance vectors associated with emission and scattering indices for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) has been performed to examine the regional variability in relations between brightness temperature and rain rate over portions of the tropical oceans known to exhibit regional differences due to different thermodynamic environments and different large-scale forcing. The TMI indices and rain rates used in this study are the products of the Goddard profiling algorithm (GPROF), version 6. The EOF framework reduces the nine-dimensional space of the brightness temperatures and their polarizations to just two dimensions associated with the EOF coefficients. Vertical profiles of reflectivity from the TRMM precipitation radar (PR) are used to show that the statistically obtained EOFs represent bulk physical characteristics of raining clouds. Hence, EOF analysis provides an efficient framework for diagnosing regional differences in cloud structures that affect brightness temperature–rain-rate relations. The EOF framework revealed fundamental differences in the behavior of TMI surface rain-rate retrievals versus retrievals that are based on the PR aboard the TRMM satellite. In EOF space, TMI rain rates were bimodally distributed, with one mode indicating higher rain rates with greater high-density ice and rainwater content in the cloud and the other mode being consistent with moderately heavy warm rain from shallow convection. In contrast, the PR rain-rate distribution showed high rain rates being assigned over a much greater diversity of cloud structures. The manifold of EOF space constructively shows that, of the regions examined here, the tropical northwestern Pacific Ocean region produces the greatest occurrence of particularly strong cumulonimbus clouds.

Observations of Wintertime U.S. West Coast Precipitating Systems with W-Band Satellite Radar and Other Spaceborne Instruments

Abstract The potential of CloudSat W-band radar for observing wintertime storms affecting the West Coast of North America is evaluated. Storms having high hydrological impact often result from landfalls of “atmospheric rivers” (“ARs”), which are the narrow elongated regions of water vapor transport from the tropics. CloudSat measurements are used for retrievals of rain rate R and cloud ice water path (IWP) along the satellite ground track over ocean and land. These retrievals present quasi-instantaneous vertical cross sections of precipitating systems with high-resolution information about hydrometeors. This information is valuable in coastal areas with complex terrain where observations with existing instrumentation, including ground-based radars, are limited. CloudSat reflectivity enhancements [i.e., bright band (BB)] present a way to estimate freezing levels, indicating transitions between rainfall and snowfall. CloudSat estimates of these levels were validated using data from radiosonde soundings and compared to model and microwave sounder data. Comparisons of CloudSat retrievals of rain rates with estimates from ground-based radars in the areas where measurements from these radars were available indicated an agreement within retrieval uncertainties, which were around 50%. The utility of CloudSat was illustrated for case studies of pronounced AR events at landfall and over ocean. Initial analysis of CloudSat crossings of ARs during the 2006/07 season were used for rainfall regime prevalence assessment. It indicated that stratiform rain, which often had BB features, warm rain, and mixed rain were observed with about 26%, 24%, and 50% frequency. Stratiform regions generally had higher rain rates. Significant correlation (~0.72) between mean values of IWP and rain rate was observed for stratiform rainfall.

Two-Parameter Gamma Drop Size Distribution Models for Singapore

Gamma model is fitted using the second, fourth, and sixth moments to model the rain drop size distribution (DSD) of Singapore. As the Joss distrometer measures the number of rain drops between the drop diameters from 0.3 to 5 mm, the truncated moment fitting between these drop diameter ranges is also used for modeling the DSD. Gamma DSD requires three-parameter estimation:N0, the intercept parameter; μ, the shape parameter; and Λ, the slope parameter. The aim of this paper is to find a suitable fixed μ and derive an appropriate μ-Λ relation for the tropical region in order to form a two-parameter gamma model. To find an appropriate μ value, observed DSDs are fitted with different μ values to estimate the rain rates, which are assessed by rain rate observations of the distrometer. Shape-slope relationships are fitted for different categories according to the rain rate and the number of drops. The derived μ-Λ relationships for the Singapore region are compared to the published results from two other regions, and the analysis is presented. Two-parameter gamma models are compared by retrieving the rain rate using the polarimetric radar variables. The effect of truncation on rain rate retrieval is also studied, and the use of the μ-Λ relationship for rain retrieval is recommended for the tropical region. The μ-Λ relation using the truncated moment method for the rain category R ≥ 5 mm/hr and rain counts ≥ 1000 drops retrieves the rain rates well compared to other μ-Λ relations.

Satellite and Numerical Model Investigation of Two Heavy Rain Events over the Central Mediterranean

Abstract Two heavy rain events over the Central Mediterranean basin, which are markedly different by genesis, dimensions, duration, and intensity, are analyzed. Given the relative low frequency of this type of severe storms in the area, a synoptic analysis describing their development is included. A multispectral analysis based on geostationary multifrequency satellite images is applied to identify cloud type, hydrometeor phase, and cloud vertical extension. Precipitation intensity is retrieved from (i) surface rain gauges, (ii) satellite data, and (iii) numerical model simulations. The satellite precipitation retrieval algorithm 183-Water vapor Strong Lines (183-WSL) is used to retrieve rain rates and cloud hydrometeor type, classify stratiform and convective rainfall, and identify liquid water clouds and snow cover from the Advanced Microwave Sounding Unit-B (AMSU-B) sensor data. Rainfall intensity is also simulated with the Weather Research and Forecasting (WRF) numerical model over two nested domains with horizontal resolutions of 16 km (comparable to that of the satellite sensor AMSU-B) and 4 km. The statistical analysis of the comparison between satellite retrievals and model simulations demonstrates the skills of both methods for the identification of the main characteristics of the cloud systems with a suggested overall bias of the model toward very low rain intensities. WRF (in the version used for the experiment) seems to classify as low rain intensity regions those areas where the 183-WSL retrieves no precipitation while sensing a mixture of freshly nucleated cloud droplets and a large amount of water vapor; in these areas, especially adjacent to the rain clouds, large amounts of cloud liquid water are detected. The satellite method performs reasonably well in reproducing the wide range of gauge-detected precipitation intensities. A comparison of the 183-WSL retrievals with gauge measurements demonstrates the skills of the algorithm in discriminating between convective and stratiform precipitation using the scattering and absorption of radiation by the hydrometeors.

Applicability of the Iterative Backward Retrieval Method for the GPM Dual-Frequency Precipitation Radar

The Dual-frequency Precipitation Radar (DPR) on the core satellite of the Global Precipitation Measurement mission will measure the radar reflectivity factor in the Ku- and Ka-bands. A rain-rate retrieval algorithm that does not require a surface reference was developed (called the MA04 method). However, MA04 cannot give the true solution in some cases of heavy rainfall. MA04 is a simplified version of the iterative backward retrieval method (IBRM), and the IBRM is equivalent to the forward retrieval method with a constraint. The purpose of this study is to clarify the essential conditions under which the IBRM and MA04 can give the true solution (the conditions are referred to as “the applicability” as in the title). For the purpose, DPR measurements are simulated under simplified assumptions. The applicability of the IBRM and MA04 is closely related to the magnitude of internal attenuation. The upper limit of rain rate for which the IBRM can obtain a true solution is 10 to 20 mm · h <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> if the internal attenuation occurs between the top and middle of the target range bin and the vertical resolution is 0.25 km. The upper limit of rain rate for which MA04 can obtain a true solution is dependent on the number of range bins, and it is 24 to 36 mm · h <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> in the case of n = 12. MA04 can apply a wider range of rainfall than the IBRM because MA04 tends to select the solution with the smallest attenuation among possible solutions.

Evaluation of rainfall retrievals from SEVIRI reflectances over West Africa using TRMM-PR and CMORPH

Abstract. This paper describes the evaluation of the KNMI Cloud Physical Properties – Precipitation Properties (CPP-PP) algorithm over West Africa. The algorithm combines condensed water path (CWP), cloud phase (CPH), cloud particle effective radius (re), and cloud-top temperature (CTT) retrievals from visible, near-infrared and thermal infrared observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellites to estimate rain occurrence frequency and rain rate. For the 2005 and 2006 monsoon seasons, it is investigated whether the CPP-PP algorithm is capable of retrieving rain occurrence frequency and rain rate over West Africa with sufficient accuracy, using Tropical Monsoon Measurement Mission Precipitation Radar (TRMM-PR) as reference. As a second goal, it is assessed whether SEVIRI is capable of monitoring the seasonal and daytime evolution of rainfall during the West African monsoon (WAM), using Climate Prediction Center Morphing Technique (CMORPH) rainfall observations. The SEVIRI-detected rainfall area agrees well with TRMM-PR, with the areal extent of rainfall by SEVIRI being ~10% larger than from TRMM-PR. The mean retrieved rain rate from CPP-PP is about 8% higher than from TRMM-PR. Examination of the TRMM-PR and CPP-PP cumulative frequency distributions revealed that differences between CPP-PP and TRMM-PR are generally within +/−10%. Relative to the AMMA rain gauge observations, CPP-PP shows very good agreement up to 5 mm h−1. However, at higher rain rates (5–16 mm h−1) CPP-PP overestimates compared to the rain gauges. With respect to the second goal of this paper, it was shown that both the accumulated precipitation and the seasonal progression of rainfall throughout the WAM is in good agreement with CMORPH, although CPP-PP retrieves higher amounts in the coastal region of West Africa. Using latitudinal Hovmüller diagrams, a fair correspondence between CPP-PP and CMORPH was found, which is reflected by high correlation coefficients (~0.7) for both rain rate and rain occurrence frequency. The daytime cycle of rainfall from CPP-PP shows distinctly different patterns for three different regions in West Africa throughout the WAM, with a decrease in dynamical range of rainfall near the Inter Tropical Convergence Zone (ITCZ). The dynamical range as retrieved from CPP-PP is larger than that from CMORPH. It is suggested that this results from both the better spatio-temporal resolution of SEVIRI, as well as from thermal infrared radiances being partly used by CMORPH, which likely smoothes the daytime precipitation signal, especially in case of cold anvils from convective systems. The promising results show that the CPP-PP algorithm, taking advantage of the high spatio-temporal resolution of SEVIRI, is of added value for monitoring daytime precipitation patterns in tropical areas.

CloudSat measurements of landfalling hurricanes Gustav and Ike (2008)

[1] The spaceborne 94 GHz radar onboard the CloudSat polar orbiting satellite offers new opportunities in estimating parameters of precipitating cloud systems including hurricanes. CloudSat measurements can resolve the vertical extent of storms and hurricanes, thus providing a view of their complex structure. These measurements can be used to retrieve with high spatial resolution rainfall and ice content parameters in an atmospheric vertical column as the satellite moves over the precipitating systems. Two major Atlantic hurricanes of the 2008 season, Gustav and Ike, were observed by CloudSat near their landfalls on the coast of the Gulf of Mexico. CloudSat measurements indicated cloud top heights at around 14–16 km above the ground and extended areas of startiform-like precipitation with estimated rain rates in a range of about 3–12 mm h−1. The radar bright band features observed just below the freezing level were identifiable for most measured reflectivity profiles. Maximum retrieved ice water path values reached about 20,000 g m−2. CloudSat rain rate retrievals over land and water agreed reasonably well with approximately coincident estimates from surface precipitation radars in the areas where measurements from these radars were available and were not contaminated by melting layer returns. Hurricane Gustav remnants observed by CloudSat several days after the landfall were characterized by smaller values of rain rate and ice water path (∼2–3 mm h−1 and 1000–10,000 g m−2, correspondingly). The hurricane parameters retrieved from CloudSat measurements may complement data from other satellite sources, which are traditionally used for hurricane observations.

The 183-WSL fast rain rate retrieval algorithm: Part I: Retrieval design

The Water vapour Strong Lines at 183GHz (183-WSL) fast retrieval method retrieves rain rates and classifies precipitation types for applications in nowcasting and weather monitoring. The retrieval scheme consists of two fast algorithms, over land and over ocean, that use the water vapour absorption lines at 183.31GHz corresponding to the channels 3 (183.31±1GHz), 4 (183.31±3GHz) and 5 (183.31±7GHz) of the Advanced Microwave Sounding Unit module B (AMSU-B) and of the Microwave Humidity Sounder (MHS) flying on NOAA-15-18 and Metop-A satellite series, respectively.The method retrieves rain rates by exploiting the extinction of radiation due to rain drops following four subsequent steps. After ingesting the satellite data stream, the window channels at 89 and 150GHz are used to compute scattering-based thresholds and the 183-WSLW module for rainfall area discrimination and precipitation type classification as stratiform or convective on the basis of the thresholds calculated for land/mixed and sea surfaces. The thresholds are based on the brightness temperature difference Δwin=TB89−TB150 and are different over land (L) and over sea (S): cloud droplets and water vapour (Δwin<3K L; Δwin<0K S), stratiform rain (3K<Δwin<10K L; 0K<Δwin<10K S), and convective rain (Δwin>10K L and S). The thresholds, initially empirically derived from observations, are corroborated by the simulations of the RTTOV radiative transfer model applied to 20000 ECMWF atmospheric profiles at midlatitudes and the use of data from the Nimrod radar network. A snow cover mask and a digital elevation model are used to eliminate false rain area attribution, especially over elevated terrain. A probability of detection logistic function is also applied in the transition region from no-rain to rain adjacent to the clouds to ensure continuity of the rainfall field. Finally, the last step is dedicated to the rain rate retrieval with the modules 183-WSLS (stratiform) and 183WSLC (convective), and the module 183-WSL for total rainfall intensity derivation.A comparison with rainfall retrievals from the Goddard Profiling (GPROF) TRMM 2A12 algorithm is done with good results on a stratiform and hurricane case studies. A comparison is also conducted with the MSG-based Precipitation Index (PI) and the Scattering Index (SI) for a convective-stratiform event showing good agreement with the 183-WSLC retrieval. A complete validation of the product is the subject of Part II of the paper.

Synergetic use of millimeter- and centimeter-wavelength radars for retrievals of cloud and rainfall parameters

Abstract. A remote sensing approach for simultaneous retrievals of cloud and rainfall parameters in the vertical column above the US Department of Energy's (DOE) Climate Research Facility at the Tropical Western Pacific (TWP) Darwin site in Australia is described. This approach uses vertically pointing measurements from a DOE Ka-band radar and scanning measurements from a nearby C-band radar pointing toward the TWP Darwin site. Rainfall retrieval constraints are provided by data from a surface impact disdrometer. The approach is applicable to stratiform precipitating cloud systems when a separation between the liquid hydrometeor layer, which contains rainfall and liquid water clouds, and the ice hydrometeor layer is provided by the radar bright band. Absolute C-band reflectivities and Ka-band vertical reflectivity gradients in the liquid layer are used for retrievals of the mean layer rain rate and cloud liquid water path (CLWP). C-band radar reflectivities are also used to estimate ice water path (IWP) in regions above the melting layer. The retrieval uncertainties of CLWP and IWP for typical stratiform precipitation systems are about 500–800 g m−2 (for CLWP) and a factor of 2 (for IWP). The CLWP retrieval uncertainties increase with rain rate, so retrievals for higher rain rates may be impractical. The expected uncertainties of layer mean rain rate retrievals are around 20%, which, in part, is due to constraints available from the disdrometer data. The applicability of the suggested approach is illustrated for two characteristic events observed at the TWP Darwin site during the wet season of 2007. A future deployment of W-band radars at the DOE tropical Climate Research Facilities can improve CLWP estimation accuracies and provide retrievals for a wider range of stratiform precipitating cloud events.