Surface Reference Technique Research Articles

Understanding Errors in Cloud Liquid Water Path Retrievals Derived from CloudSat Path-Integrated Attenuation

Abstract An algorithm that derives the nonprecipitating cloud liquid water path Wcld from CloudSat using a surface reference technique (SRT) is presented. The uncertainty characteristics of the SRT are evaluated. It is demonstrated that an accurate analytical formulation for the pixel-scale precision can be derived. The average precision of the SRT is estimated to be 34 g m−2 at the individual pixel scale; however, precision systematically decreases from around 30 to 40 g m−2 as cloud fraction varies from 0% to 100%. The retrievals of clear-sky Wcld have a mean bias of 0.9 g m−2. Output from a large-eddy simulation coupled to a radar simulator shows that an additional bias of −8% may result from nonuniformity within the footprint of cloudy pixels. The retrieval yield for the SRT, measured relative to all warm clouds over ocean between 60°N and 60°S latitude is 43%. The SRT Wcld is compared with one estimate of Wcld from the Moderate Resolution Imaging Spectroradiometer (MODIS) using an adiabatic cloud profile and an effective radius derived from 3.7-μm reflectance. A strong correlation between the mean MODIS Wcld and SRT Wcld is found across diverse cloud regimes, but with biases in the mean Wcld that are cloud-regime dependent. Overall, the mean bias of the SRT relative to MODIS is −13.1 g m−2. Systematic underestimates of Wcld by the SRT resulting from nonuniform beamfilling cannot be ruled out as an explanation for the retrieval bias.

Correction of Path-Integrated Attenuation Estimates Considering the Soil Moisture Effect for the GPM Dual-Frequency Precipitation Radar

Abstract Spaceborne precipitation radars, including the Tropical Rainfall Measuring Mission’s Precipitation Radar (PR) and the Global Precipitation Measurement Mission’s Dual-Frequency Precipitation Radar (DPR), measure not only precipitation echoes but surface echoes as well, the latter of which are used to estimate the path-integrated attenuation (PIA) in the surface reference technique (SRT). In our previous study based on analyzing PR measurements, we found that attenuation-free surface backscattering cross sections (denoted by ) over land increased in the presence of precipitation. This behavior, called the soil moisture effect, causes an underestimate of the PIA by the SRT as the method does not explicitly consider this effect. In this study, measurements made by Ku-band Precipitation Radar (KuPR) and Ka-band Precipitation Radar (KaPR), which comprise the DPR, were analyzed to examine whether KuPR and KaPR exhibit similar dependencies on the soil moisture as does the PR. For both KuPR and KaPR, an increase in was observed for a large portion of the land area, except for forests and deserts. Results from the Hitschfeld–Bordan (HB) method suggest that increases with the surface precipitation rate for light precipitation events. Meanwhile, for heavy precipitation, owing to the degradation of the HB method, it is difficult to estimate quantitatively. Thus, a correction method for PIA that considers the soil moisture effect was developed and implemented into the DPR standard algorithm. With this correction, the surface precipitation rate estimates increased by approximately 18% for KuPR and 15% for the normal scan of KaPR over land.

Stability of the Dual-Frequency Radar Equations and a New Method Applied to the GPM’s Dual-Frequency Precipitation Radar (DPR) Data

A new algorithm is proposed, which estimates two parameters of the particle size distribution (PSD) at each range bin from the global precipitation measurement&#x2019;s (GPM&#x2019;s) dual-frequency precipitation radar (DPR) data. The equation that expresses the relationship of the PSD parameters between adjacent range bins is derived. By including the attenuation effect within the bin in the discretized equation, the new algorithm alleviates the double-solution problem when attenuation within the bin is sufficiently large. The stability of the solutions to the equation depends on the value of the mean diameter <inline-formula> <tex-math notation="LaTeX">$D_{m}$ </tex-math></inline-formula> and its gradient with respect to the range in the case of liquid precipitation. There is a critical diameter above which the backward processing of the equation provides a stable or moderately diverging solution, unlike the forward processing that often gives unstable solutions. To provide a set of initial conditions without using the surface reference technique (SRT) in the backward processing, an initialization method using the Hitschfeld&#x2013;Bordan (HB) attenuation correction method is proposed and tested. The proposed algorithm may provide a tool for investigating the assumptions used in various algorithms.

Relating GPM Radar Reflectivity Profile Characteristics to Path-Integrated Attenuation

The Global Precipitation Measurement (GPM) mission was launched in February 2014; its dual-frequency precipitation radar (DPR) operates at both Ku- and Ka-band s. Attenuation in precipitation is typically not negligible, especially at Ka-band. Hence, attenuation correction is an important part of the GPM DPR retrieval algorithm. The operational algorithm uses a path-integrated attenuation (PIA) obtained by comparing the measured surface return with that expected either from a nearby, nonprecipitating area or from the same area, acquired at a previous, nonprecipitating time. This surface reference technique has worked well in most situations but can result in erroneously low estimates of the path attenuation in situations with nonuniform filling of the radar beam, especially at Ka-band due to its larger attenuation. This paper explores the existence of relationships between the Ka-band PIA and the characteristics of the measured reflectivity profiles. The author finds that PIA is, indeed, related to reflectivity profiles, with strongest correlation between the PIA and the measured rainfall dual-frequency reflectivity ratio just above the surface. This relationship could be used as an estimator in the cases with severe nonuniform beam filling.

Surface Echo Characteristics Derived From the Wide Swath Experiment of the Precipitation Radar Onboard TRMM Satellite During Its End-of-Mission Operation

The purpose of this paper is to assess the height and strength of the surface echo clutter from the wide swath operation of a future spaceborne precipitation radar (PR) using the wide swath observation data during the end-of-mission experiment of the PR onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. In this experiment, the maximum incident angle was expanded up to 32.5° from nadir, while the maximum incident angle is 18° for normal observation of the TRMM/PR. Although the results show that the clutter height monotonically increases with the incident angle as expected, the clutter height is suppressed for wide angles because of the weakening of the surface echo strength. As a result, the clutter height is less than 2 km if the rain echo is 15 dB higher than the noise level (i.e., about 34 dBZ echo for the TRMM/PR). The clutter height and the normalized backscattering cross section ( $\sigma ^{\mathrm {\mathbf {0}}}$ ) are compared between ocean and land. Suppression of the clutter height is significant over ocean because of the relatively smaller $\sigma ^{\mathrm {\mathbf {0}}}$ (smoother surface) and flat surface (no topography). The impact of the $\sigma ^{\mathrm {\mathbf {0}}}$ for wide swath observation on the rain retrieval algorithm, especially on the surface reference technique, is also examined. The operation with a swath width almost twice the current TRMM/PR is achievable if relatively intense echoes are targeted; however, relatively weak and shallow precipitation will be masked by the clutter.

A Self-Consistent Numerical Method for Microphysical Retrieval in Rain Using GPM Dual-Wavelength Radar

AbstractAn assessment of the performance of a self-consistent numerical method for dual-frequency radar based on the retrieval of microphysical precipitation parameter profiles is presented. From the surface reference technique (SRT), the estimation of path-integrated attenuation (PIA) is performed at both wavelengths and reflectivity factors are corrected for attenuation. Then, solving numerically a system of two nonlinear differential equations, the drop size distribution (DSD) parameters are obtained. The method is applied only in the stratiform rain region, from the surface along the path upward to the brightband bottom.Assuming a gamma DSD model to describe the distribution of precipitation found in nature, a methodology has been developed to transform the estimated DSD provided by a vertically pointing Micro Rain Radar to a profile given by a ground-based Ku- and Ka-band radar, and then in a spaceborne dual-frequency radar measurement profile.Under ideal conditions in which the different errors that simultaneously affect the retrieval of precipitation microphysical parameters may be individually studied, particular emphasis has been placed on the incidence of variability due to the DSD shape parameter μ, the presence of uncertainties in PIA estimates, and radar signal fluctuations.To achieve an appropriate level of confidence in the simulation outputs, a qualitative indirect method of validation was realized by comparing the results obtained by the simulation with the experimental ones and weighing how consistent they are with what the theory implies. GPM near-real-time data from an entire year (October 2014–September 2015) were used for this purpose.

Improved Retrieval of Cloud Liquid Water from CloudSat and MODIS

AbstractA revised version of the CloudSat–MODIS cloud liquid water retrieval algorithm is presented. The new algorithm, which combines measurements of radar reflectivity and cloud optical depth, addresses issues discovered in the current CloudSat–MODIS cloud water content (CWC) product. This current product is shown to be underconstrained by observations and to be too dependent on prior information incorporated into the Bayesian optimal-estimation algorithm. The most significant change made to the algorithm in this study was decreasing the number of independent variables to allow the observations to constrain the retrieved values better. The retrieval was also reformulated for improved compliance with the mathematical assumptions of the optimal-estimation algorithm. To validate the accuracy of the revised algorithm, the path-integrated attenuation (PIA) of the CloudSat radar signal was computed from the algorithm results. These modeled values were compared with independent measurements of the PIA that were obtained using a surface reference technique. This comparison shows that the cloud liquid water retrieved by the algorithm is close to being unbiased. The revised algorithm was also found to be an improvement over the current CloudSat CWC product and, to a lesser degree, the MODIS-derived cloud liquid water path.

An Initial Assessment of the Surface Reference Technique Applied to Data from the Dual-Frequency Precipitation Radar (DPR) on the GPM Satellite

AbstractIt has long been recognized that path-integrated attenuation (PIA) can be used to improve precipitation estimates from high-frequency weather radar data. One approach that provides an estimate of this quantity from airborne or spaceborne radar data is the surface reference technique (SRT), which uses measurements of the surface cross section in the presence and absence of precipitation. Measurements from the dual-frequency precipitation radar (DPR) on the Global Precipitation Measurement (GPM) satellite afford the first opportunity to test the method for spaceborne radar data at Ka band as well as for the Ku-band–Ka-band combination.The study begins by reviewing the basis of the single- and dual-frequency SRT. As the performance of the method is closely tied to the behavior of the normalized radar cross section (NRCS or σ0) of the surface, the statistics of σ0 derived from DPR measurements are given as a function of incidence angle and frequency for ocean and land backgrounds over a 1-month period. Several independent estimates of the PIA, formed by means of different surface reference datasets, can be used to test the consistency of the method since, in the absence of error, the estimates should be identical. Along with theoretical considerations, the comparisons provide an initial assessment of the performance of the single- and dual-frequency SRT for the DPR. The study finds that the dual-frequency SRT can provide improvement in the accuracy of path attenuation estimates relative to the single-frequency method, particularly at Ku band.

Intercomparison of Attenuation Correction Methods for the GPM Dual-Frequency Precipitation Radar

AbstractA new attenuation correction method has been developed for the dual-frequency precipitation radar (DPR) on the core satellite of the Global Precipitation Measurement (GPM) mission. The new method is based on Hitschfeld and Bordan’s attenuation correction method (HB method), but the relationship between the specific attenuation k and the effective radar reflectivity factor Ze (k–Ze relationship) is modified by using the dual-frequency ratio (DFR) of Ze and the surface reference technique (SRT). Therefore, the new method is called the HB-DFR-SRT method (H-D-S method). The previous attenuation correction method, called the HB-DFR method (H-D method), results in an underestimation of precipitation rates for heavy precipitation, but the H-D-S method mitigates the negative bias by means of the SRT. When only a single-frequency measurement is available, the H-D-S method is identical to the HB-SRT method (H-S method).The attenuation correction methods were tested with a simple synthetic DPR dataset. As long as the SRT gives perfect estimates of path-integrated attenuation and the adjustment factor of the k–Ze relationship (denoted by ε) is vertically constant, the H-S method is much better than the dual-frequency methods. Tests with SRT error and vertical variation in ε showed that the H-D method was better than the H-S method for weak precipitation, whereas the H-S method was better than the H-D method for heavy precipitation. The H-D-S method did not produce the best results for both weak and heavy precipitation, but the results are stable. Quantitative evaluation should be done with real DPR measurement datasets.

GPMとTRMMのマッチアップ事例におけるKuPRアルゴリズムの評価

GPM Core Observatory satellite has been in operation since February, 2014. Before the GPM operation started, TRMM had observed the rain rate in tropical regions. GPM's KuPR is a successor of TRMM/PR, and both have the same design [1]. However, differences have been confirmed in their estimates for the same rainfall. Therefore, we focus on the retrieval method of rain rate, and compare the rain rate estimates by KuPR and PR. As a result of comparison, path-integrated attenuation (PIA), surface reference technique (SRT) and non-uniform beam filling (NUBF) are regarded as the factors of differences of the rain rate. Particularly, overestimation of PIA in KuPR is one of the most important factors of the differences of rain rate. On the other hand, rain rate is overestimated in KuPR when SRT is ignored. Therefore, the number of cases is needed to be increased, and it is necessary to improve the parameters of SRT and NUBF.

二周波降水レーダによる雨滴粒径分布推定

Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Mission (GPM) core satellite started its precipitation measurement in March 2014, and the DPR standard products are available in public since September 2014. DPR consists of two radars, KuPR (13.6GHz) and KaPR (35.5GHz), and the DPR standard product consists of KuPR single-frequency product, KaPR single-frequency product, and dual-frequency product. Drop size distribution (DSD) and precipitation rates estimated by dual-frequency product are expected to be better than those by single-frequency product. However, the dual-frequency product has not been validated well. In this study, DSD and precipitation rate estimates by the dual-frequency method are compared with those in KuPR single-frequency product with surface reference technique. They are not agreed well partly because attenuation correction is not well applied for KaPR. For heavy precipitation, as attenuation correction is difficult, dual-frequency method may not be expected to work well, but the dual-frequency method should be applied mainly for weak and medium precipitation.

Application of active spaceborne remote sensing for understanding biases between passive cloud water path retrievals

AbstractBias between the Advanced Microwave Scanning Radiometer–EOS (AMSR‐E) version 2 and the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 5.1 cloud liquid water path (Wc) products are explored with the aid of coincident active observations from the CloudSat radar and the CALIPSO lidar. In terms of detection, the active observations provide precise separation of cloudy from clear sky and precipitating from nonprecipitating clouds. In addition, they offer a unique quantification of precipitation water path (Wp) in warm clouds. They also provide an independent quantification of Wc that is based on an accurate surface reference technique, which is an independent arbiter between the two passive approaches. The results herein establish the potential for CloudSat and CALIPSO to provide an independent assessment of bias between the conventional passive remote sensing methods from reflected solar and emitted microwave radiation. After applying a common data filter to the observations to account for sampling biases, AMSR‐E is biased high relative to MODIS in the global mean by 26.4 gm−2. The RMS difference in the regional patterns is 32.4 gm−2, which highlights a large geographical dependence in the bias which is related to the tropical transitions from stratocumulus to cumulus cloud regimes. The contributions of four potential sources for this bias are investigated by exploiting the active observations: (1) bias in MODIS related to solar zenith angle dependence accounts for −2.3 gm−2, (2) bias in MODIS due to undersampling of cloud edges accounts for 4.2 gm−2, (3) a wind speed and water vapor‐dependent “clear‐sky biase” in the AMSR‐E retrieval accounts for 6.3 gm−2, and (4) evidence suggests that much of the remaining 18 gm−2 bias is related to the assumed partitioning of the observed emission signal between cloud and precipitation water in the AMSR‐E retrieval. This is most evident through the correlations between the regional mean patterns of Wp and the Wc bias within the latitudes of 30°N and 30°S, suggesting that the assumption of a regionally invariant cloud/precipitation partitioning in the AMSR‐E algorithm is the likely causal factor.

Evaluation of Spatial Errors of Precipitation Rates and Types from TRMM Spaceborne Radar over the Southern CONUS

Abstract In this paper, the authors estimate the uncertainty of the rainfall products from NASA and Japan Aerospace Exploration Agency's (JAXA) Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) so that they may be used in a quantitative manner for applications like hydrologic modeling or merging with other rainfall products. The spatial error structure of TRMM PR surface rain rates and types was systematically studied by comparing them with NOAA/National Severe Storms Laboratory's (NSSL) next generation, high-resolution (1 km/5 min) National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (QPE; NMQ/Q2) over the TRMM-covered continental United States (CONUS). Data pairs are first matched at the PR footprint scale (5 km/instantaneous) and then grouped into 0.25° grid cells to yield spatially distributed error maps and statistics using data from December 2009 through November 2010. Careful quality control steps (including bias correction with rain gauges and quality filtering) are applied to the ground radar measurements prior to considering them as reference data. The results show that PR captures well the spatial pattern of total rainfall amounts with a high correlation coefficient (CC; 0.91) with Q2, but this decreases to 0.56 for instantaneous rain rates. In terms of precipitation types, Q2 and PR convective echoes are spatially correlated with a CC of 0.63. Despite this correlation, PR's total annual precipitation from convection is 48.82% less than that by Q2, which points to potential issues in the PR algorithm's attenuation correction, nonuniform beam filling, and/or reflectivity-to-rainfall relation. Finally, the spatial analysis identifies regime-dependent errors, in particular in the mountainous west. It is likely that the surface reference technique is triggered over complex terrain, resulting in high-amplitude biases.

Modified Hitschfeld–Bordan Equations for Attenuation-Corrected Radar Rain Reflectivity: Application to Nonuniform Beamfilling at Off-Nadir Incidence

Abstract As shown by Takahashi et al., multiple path attenuation estimates over the field of view of an airborne or spaceborne weather radar are feasible for off-nadir incidence angles. This follows from the fact that the surface reference technique, which provides path attenuation estimates, can be applied to each radar range gate that intersects the surface. This study builds on this result by showing that three of the modified Hitschfeld–Bordan estimates for the attenuation-corrected radar reflectivity factor can be generalized to the case where multiple path attenuation estimates are available, thereby providing a correction to the effects of nonuniform beamfilling. A simple simulation is presented showing some strengths and weaknesses of the approach.

Comparison of Drop Size Distribution Parameter (D0) and Rain Rate from S-Band Dual-Polarized Ground Radar, TRMM Precipitation Radar (PR), and Combined PR–TMI: Two Events from Kwajalein Atoll

Abstract The estimation of the drop size distribution parameter [median volume diameter (D0)] and rain rate (R) from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) as well as from combined PR–TRMM Microwave Imager (TMI) algorithms are considered in this study for two TRMM satellite overpasses near the Kwajalein Atoll. An operational dual-polarized S-band radar (KPOL) located in Kwajalein is central as the only TRMM ground validation site for measurement of precipitation over the open ocean. The accuracy of the TRMM PR in retrieving D0 and R is better for precipitation over the ocean based on a more stable surface reference technique for estimating the path-integrated attenuation. Also, combined PR–TMI methods are more accurate over the open ocean because of better knowledge of the surface microwave emissivity. Using Zh (horizontal polarized radar reflectivity) and Zdr (differential reflectivity) data for the two TRMM overpass events over Kwajalein, D0 and R from KPOL are retrieved. Herein, the main objective is to see if the D0 retrieved from either PR or the combined PR–TMI algorithms are in agreement with KPOL-derived values. Also, the variation of D0 versus R is compared for convective rain pixels from KPOL, PR, and PR–TMI. It is shown that the PR–TMI optimal estimation scheme does indeed adjust the D0 in the “correct” direction, on average, from the a priori state if the KPOL data are considered to be the ground truth. This correct adjustment may be considered as evidence of the value added by the TMI brightness temperatures in the combined PR–TMI variational scheme, at least for the two overpass events considered herein.

Assessment of the Performance of a Dual-Frequency Surface Reference Technique Over Ocean

The high correlation of the rain-free surface cross sections at two frequencies suggests that the estimate of differential path-integrated attenuation caused by precipitation along the radar beam can be obtained to a higher degree of accuracy than the path attenuation at either frequency. We explore this potential first analytically and then by examining data from the JPL dual-frequency airborne radar using measurements from the Tropical Composition, Cloud, and Climate Coupling experiment obtained during July-August 2007. Despite an improvement in the accuracy of the differential path attenuation, solving for parameters of the particle size distribution often requires not only this quantity but the single-wavelength path attenuation as well. We investigate a simple method of estimating the single-frequency path attenuation from the differential attenuation and compare this estimate with that derived directly from the surface return.

A Robust Dual-Frequency Radar Profiling Algorithm

In this study, an algorithm to retrieve precipitation from spaceborne dual-frequency (13.8 and 35.6 GHz, or Ku/Ka band) radar observations is formulated and investigated. Such algorithms will be of paramount importance in deriving radar-based and combined radar–radiometer precipitation estimates from observations provided by the forthcoming NASA Global Precipitation Measurement (GPM) mission. In GPM, dual-frequency Ku-/Ka-band radar observations will be available only within a narrow swath (approximately one-half of the width of the Ku-band radar swath) over the earth’s surface. Therefore, a particular challenge is to develop a flexible radar retrieval algorithm that can be used to derive physically consistent precipitation profile estimates across the radar swath irrespective of the availability of Ka-band radar observations at any specific location inside that swath, in other words, an algorithm capable of exploiting the information provided by dual-frequency measurements but robust in the absence of Ka-band channel. In the present study, a unified, robust precipitation retrieval algorithm able to interpret either Ku-only or dual-frequency Ku-/Ka-band radar observations in a manner consistent with the information content of the observations is formulated. The formulation is based on 1) a generalized Hitschfeld–Bordan attenuation correction method that yields generic Ku-only precipitation profile estimates and 2) an optimization procedure that adjusts the Ku-band estimates to be physically consistent with coincident Ka-band reflectivity observations and surface reference technique–based path-integrated attenuation estimates at both Ku and Ka bands. The algorithm is investigated using synthetic and actual airborne radar observations collected in the NASA Tropical Composition, Cloud, and Climate Coupling (TC4) campaign. In the synthetic data investigation, the dual-frequency algorithm performed significantly better than a single-frequency algorithm; dual-frequency estimates, however, are still sensitive to various assumptions such as the particle size distribution shape, vertical and cloud water distributions, and scattering properties of the ice-phase precipitation.

A Robust Dual-Frequency Radar Profiling Algorithm

AbstractIn this study, an algorithm to retrieve precipitation from spaceborne dual-frequency (13.8 and 35.6 GHz, or Ku/Ka band) radar observations is formulated and investigated. Such algorithms will be of paramount importance in deriving radar-based and combined radar–radiometer precipitation estimates from observations provided by the forthcoming NASA Global Precipitation Measurement (GPM) mission. In GPM, dual-frequency Ku-/Ka-band radar observations will be available only within a narrow swath (approximately one-half of the width of the Ku-band radar swath) over the earth’s surface. Therefore, a particular challenge is to develop a flexible radar retrieval algorithm that can be used to derive physically consistent precipitation profile estimates across the radar swath irrespective of the availability of Ka-band radar observations at any specific location inside that swath, in other words, an algorithm capable of exploiting the information provided by dual-frequency measurements but robust in the absence of Ka-band channel. In the present study, a unified, robust precipitation retrieval algorithm able to interpret either Ku-only or dual-frequency Ku-/Ka-band radar observations in a manner consistent with the information content of the observations is formulated. The formulation is based on 1) a generalized Hitschfeld–Bordan attenuation correction method that yields generic Ku-only precipitation profile estimates and 2) an optimization procedure that adjusts the Ku-band estimates to be physically consistent with coincident Ka-band reflectivity observations and surface reference technique–based path-integrated attenuation estimates at both Ku and Ka bands. The algorithm is investigated using synthetic and actual airborne radar observations collected in the NASA Tropical Composition, Cloud, and Climate Coupling (TC4) campaign. In the synthetic data investigation, the dual-frequency algorithm performed significantly better than a single-frequency algorithm; dual-frequency estimates, however, are still sensitive to various assumptions such as the particle size distribution shape, vertical and cloud water distributions, and scattering properties of the ice-phase precipitation.

Identifying multiple‐scattering‐affected profiles in CloudSat observations over the oceans

Multiple scattering strongly affects the CloudSat Profiling Radar reflectivity when the satellite is overpassing moderate and heavy precipitation systems. Therefore it is important to identify profiles that may be contaminated by multiple scattering prior to interpreting the results of any application that involves the use of CloudSat data in raining scenes. On the basis of analysis of multiple‐scattering Monte Carlo reflectivity simulations applied to cloud‐resolving model‐generated microphysical profiles encompassing a large variety of precipitating systems, a relatively straightforward criterion is proposed for flagging profiles potentially affected by multiple scattering. The path‐integrated attenuation, that can be estimated from CloudSat's 2B‐GEOPROF product, can be used to identify four multiple‐scattering regimes: (1) the single scattering approximation is applicable to the entire Z‐profile; (2) the single scattering approximation is unreliable but the second order of scattering approximation is valid; (3) the second order of scattering approximation is not valid owing to higher order of multiple‐scattering effects which, however, do not affect the surface reference technique‐based path‐integrated attenuation estimates; and (4) the multiple scattering is affecting the surface return as well, thus spoiling the path‐integrated attenuation estimates. Operational path‐integrated attenuation thresholds for each of these regimes are then applied to the CloudSat data set over the global oceans, where path‐integrated attenuation estimations are more accurate than over land. Case studies and global statistics of the occurrence of multiple scattering are presented: for ocean pixels, around 80% (90%) of the profiles identified as rainy can be treated in the single scattering (second order of scattering) approximation. A threshold value around 20 dB for the one‐way path‐integrated attenuation is suggested for the applicability of the surface reference technique to the CloudSat Profiling Radar system. This roughly corresponds to 96.5% of the rainy pixels. Owing to the different precipitation regimes, results are strongly regionally and seasonally dependent. For instance in the Inter‐Tropical Convergence Zone a not negligible fraction of the raining pixels requires either second‐order (10–15%) or higher‐order scattering (∼10%) to accurately model the observed reflectivity profile.

Impact of Uncertainty in the Drop Size Distribution on Oceanic Rainfall Retrievals From Passive Microwave Observations

The variability of the drop size distribution (DSD) is one of the factors that must be considered in understanding the uncertainties in the retrieval of oceanic precipitation from passive microwave observations. Here, we have used observations from the Precipitation Radar on the Tropical Rainfall Measuring Mission spacecraft to infer the relationship between the DSD and the rain rate and the variability in this relationship. The impact on passive microwave rain rate retrievals varies with frequency and rain rate. The total uncertainty for a given pixel can be slightly larger than 10% at the low end (ca. 10 GHz) of frequencies commonly used for this purpose and smaller at higher frequencies (up to 37 GHz). Since the error is not totally random, averaging many pixels, as in a monthly rainfall total, should roughly halve this uncertainty. The uncertainty may be lower at rain rates less than about 30 mm/h, but the lack of sensitivity of the surface reference technique to low rain rates makes it impossible to tell from the present data set.