Ice Water Path Retrieval Research Articles

Retrieval of ice water path from the Microwave Humidity Sounder (MWHS) aboard FengYun-3B (FY-3B) satellite polarimetric measurements based on a deep neural network

Abstract. The ice water path (IWP) is an important cloud parameter in atmospheric radiation, and there are still great difficulties in its retrieval. Artificial neural networks have become a popular method in atmospheric remote sensing in recent years. This study presents a global IWP retrieval based on deep neural networks using the measurements from the Microwave Humidity Sounder (MWHS) aboard the FengYun-3B (FY-3B) satellite. Since FY-3B/MWHS has quasi-polarization channels at 150 GHz, the effect of the polarimetric radiance difference (PD) was also studied. A retrieval database was established using collocations between MWHS and CloudSat 2C-ICE (CloudSat and CALIPSO Ice Cloud Property Product). Then, two types of networks were trained for cloud scene filtering and IWP retrieval. For the cloud filtering network, the microwave channels show a capacity with a false alarm ratio (FAR) of 0.31 and a probability of detection (POD) of 0.61. For the IWP retrieval network, different combination inputs of auxiliaries and channels were compared. The results show that the five MWHS channels combined with scan angle, latitude, and the ocean/land mask of inputs of auxiliary variables perform best. Applying the cloud filtering network and IWP retrieval network, the final root mean squared error (RMSE) is 916.76 g m−2, the mean absolute percentage error (MAPE) is 92 %, and the correlation coefficient (CC) is 0.65. Then, a tropical cyclone case measured simultaneously by MWHS and CloudSat was chosen to test the performance of the networks, and the result shows a good correlation (0.73) with 2C-ICE. Finally, the global annual mean IWP of MWHS is very close to that of 2C-ICE, and the 150 GHz channels give a significant improvement in the midlatitudes compared to using only 183 GHz channels.

Ice water path retrievals from Meteosat-9 using quantile regression neural networks

Abstract. The relationship between geostationary radiances and ice water path (IWP) is complex, and traditional retrieval approaches are not optimal. This work applies machine learning to improve the IWP retrieval from Meteosat-9 observations, with a focus on low latitudes, training the models against retrievals based on CloudSat. Advantages of machine learning include avoiding explicit physical assumptions on the data, an efficient use of information from all channels, and easily leveraging spatial information. Thermal infrared (IR) retrievals are used as input to achieve a performance independent of the solar angle. They are compared with retrievals including solar reflectances as well as a subset of IR channels for compatibility with historical sensors. The retrievals are accomplished with quantile regression neural networks. This network type provides case-specific uncertainty estimates, compatible with non-Gaussian errors, and is flexible enough to be applied to different network architectures. Spatial information is incorporated into the network through a convolutional neural network (CNN) architecture. This choice outperforms architectures that only work pixelwise. In fact, the CNN shows a good retrieval performance by using only IR channels. This makes it possible to compute diurnal cycles, a problem that CloudSat cannot resolve due to its limited temporal and spatial sampling. These retrievals compare favourably with IWP retrievals in CLAAS, a dataset based on a traditional approach. These results highlight the possibilities to overcome limitations from physics-based approaches using machine learning while providing efficient, probabilistic IWP retrieval methods. Moreover, they suggest this first work can be extended to higher latitudes as well as that geostationary data can be considered as a complement to the upcoming Ice Cloud Imager mission, for example, to bridge the gap in temporal sampling with respect to space-based radars.

Fast Radiative Transfer Approximating Ice Hydrometeor Orientation and Its Implication on IWP Retrievals

The accurate simulation of microwave observations of clouds and precipitation are computationally challenging. A common simplification is the assumption of totally random orientation (TRO); however, studies have revealed that TRO occurs relatively infrequently in reality. A more appropriate assumption is that of azimuthally random orientation (ARO), but so far it has been a computationally expensive task. Recently a fast approximate approach was introduced that incorporates hydrometeor orientation into the assimilation of data from microwave conically scanning instruments. The approach scales the extinction in vertical (V) and horizontal (H) polarised channels to approximate ARO. In this study, the application of the approach was extended to a more basic radiative transfer perspective using the Atmospheric Radiative Transfer Simulator and the high-frequency channels of the Global Precipitation Measurement Microwave Imager (GMI). The comparison of forward simulations and GMI observations showed that with a random selection of scaling factors from a uniform distribution between 1 and 1.4–1.5, it is possible to mimic the full distribution of observed polarisation differences at 166 GHz over land and water. The applicability of this model at 660 GHz was also successfully demonstrated by means of existing airborne data. As a complement, a statistical model for polarised snow emissivity between 160 and 190 GHz was also developed. Combining the two models made it possible to reproduce the polarisation signals that were observed over all surface types, including snow and sea ice. Further, we also investigated the impact of orientation on the ice water path (IWP) retrievals. It has been shown that ignoring hydrometeor orientation has a significant negative impact (∼20% in the tropics) on retrieval accuracy. The retrieval with GMI observations produced highly realistic IWP distributions. A significant highlight was the retrieval over snow covered regions, which have been neglected in previous retrieval studies. These results provide increased confidence in the performance of passive microwave observation simulations and mark an essential step towards developing the retrievals of ice hydrometeor properties based on data from GMI, the Ice Cloud Imager (ICI) and other conically scanning instruments.

Assessing synergistic radar and radiometer capability in retrieving ice cloud microphysics based on hybrid Bayesian algorithms

Abstract. The 2017 National Academy of Sciences Decadal Survey highlighted several high-priority objectives to be pursued in the decadal timeframe, and the next-generation Cloud, Convection and Precipitation (CCP) observing system is thereby contemplated. In this study, we develop a suite of hybrid Bayesian algorithms to evaluate two CCP remote sensor candidates including a W-band cloud radar and a (sub)millimeter-wave radiometer with channels in the 118–880 GHz frequency range for capability in constraining ice cloud microphysical quantities. The algorithms address active-only, passive-only, and synergistic active–passive retrievals. The hybrid Bayesian algorithms combine the Bayesian Monte Carlo integration and optimization process to retrieve quantities with uncertainty estimates. The radar-only retrievals employ the optimal estimation methodology, while the radiometer-involved retrievals employ ensemble approaches to maximize the posterior probability density function. A priori information is obtained from the Tropical Composition, Cloud and Climate Coupling (TC4) in situ data and CloudSat radar observations. End-to-end simulation experiments are conducted to evaluate the retrieval accuracies by comparing the retrieved parameters with known values. The experiment results suggest that the radiometer measurements possess high sensitivity to ice cloud particles with large water content. The radar-only retrievals demonstrate capability in reproducing ice water content profiles, but the performance in retrieving number concentration is poor. The synergistic observations enable improved pixel-level retrieval accuracies, and the improvements in ice water path retrievals are significant. The proposed retrieval algorithms could serve as alternative methods for exploring the synergistic active and passive concept, and the algorithm framework could be extended to the inclusion of other remote sensors to further assess the CCP observing system in future studies.

Assessing Synergistic Radar and Radiometer Retrievals of Ice Cloud Microphysics for the Atmosphere Observing System (AOS) Architecture

After exploring numerous observing system designs, the NASA aerosols, clouds, convection, and precipitation (ACCP) study team arrived at the top candidate architecture referred to as the atmosphere observing system (AOS) that is composed of a suite of spaceborne instruments in two orbital inclinations to characterize the complexity of hydrometeors and aerosols in the Earth’s atmosphere. This study proposes hybrid Bayesian retrieval algorithms that combine the Monte Carlo integration (MCI) and cost function optimization approaches to quantitatively evaluate the AOS architecture for skill in constraining the ice cloud microphysical properties. The remote sensor candidates under evaluation include multiple-frequency radars with W-, Ka-, and Ku-band channels and a submillimeter-wave radiometer. Two optimization techniques, the optimal estimation method (OEM) and Markov chain Monte Carlo (MCMC), are developed to maximize the posterior distribution function to retrieve ice cloud microphysical quantities with uncertainty estimates. Observing system simulation experiments are conducted using simulated synergistic radar and radiometer observations to determine the pixel-level retrieval accuracies by comparing the retrieved parameters to the true values. Results demonstrate that the low-frequency Ka-/Ku-band radar observations are complementary to the W-band channel since they provide more constraints on the condensed cloud scenes that are composed of large particles. The brightness temperature measurements exhibit sensitivities to the ice cloud layers with large water content (WC), and the synergistic active and passive observations improve the ice water path retrieval accuracies significantly. The scores measuring how well the AOS architecture satisfies the desired retrieval uncertainties for different ice cloud geophysical variables are also derived.

Application of M5 Model Tree in Passive Remote Sensing of Thin Ice Cloud Microphysical Properties in Terahertz Region

This article presents a new method for retrieving the Ice Water Path (IWP), the median volume equivalent sphere diameter (Dme) of thin ice clouds (IWP < 100 g/m2, Dme < 80 μm) in the Terahertz band. The upwelling brightness temperature depressions caused by the ice clouds at 325.15, 448.0, 664.0 and 874.0 GHz channels are simulated by the Atmospheric Radiative Transfer Simulator (ARTS). The simulated forward radiative transfer models are taken as historical data for the M5 model tree algorithm to construct a set of piecewise functions which represent the relation of simulated brightness temperature depressions and IWP. The inversion results are optimized by an empirical relation of the IWP and the Dme for thin ice clouds which is summarized by previous studies. We inverse IWP and Dme with the simulated brightness temperature and analyze the inversion performance of selected channels. The 874.4 ± 6.0 GHz channel provides the most accurate results, because of the strong brightness temperature response to the change of IWP in the forward radiative transfer model. In order to improve the thin ice clouds IWP and Dme retrieval accuracy at the middle-high frequency channels in Terahertz band, a dual-channel inversion method was proposed that combines the 448.0± 3.0 GHz and 664.0 ± 4.2 GHz channel. The error analysis shows that the results of the 874.4 ± 6.0 GHz channel and the dual-channel inversion are reliable, and the IWP inversion results meet the error requirement range proposed by previous studies.

Global monitoring of deep convection using passive microwave observations

In this study, we present the DEEPSTORM (DEEP moiSt aTmospheric cOnvection from micRowave radioMeter) algorithm, able to retrieve ice water path (IWP) and to detect deep moist atmospheric convection (DC) from 80°S to 80°N using observations from four spaceborne passive microwave radiometers. DEEPSTORM is based on a machine learning approach and is fitted against observations from the CPR (Cloud Profiling Radar) spaceborne radar on-board CloudSat. IWP predictions show an average root mean square error of 0.27 kg/m2 and a correlation index of 0.87. DC occurrence is detected with a probability of 59% and a false alarm rate of 24%. The prediction accuracy of IWP and DC is significantly better when the IWP exceeds 0.5 kg/m2 showing that DEEPSTORM is well suited to detect and characterise the strongest DC events. Overall DC detection is more accurate in the tropics than in mid-latitudes while the IWP retrieval works better in the mid-latitudes. Two examples illustrating the potential of DEEPSTORM are presented: the IWP is retrieved during Hurricane Matthew in 2016, and a climatology of DC occurrences between September 2016 and December 2016 is presented. This work will allow building a quasi-worldwide and 20-year long database of DC occurrence and intensity.

Sensitivity Analyses for the Retrievals of Ice Cloud Properties From Radiometric and Polarimetric Measurements in Sub‐mm/mm and Infrared Bands

AbstractSimulations of the radiometric and polarimetric properties of ice clouds based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 ice cloud model (aggregates in conjunction with the gamma distribution) are performed at wavelengths in the Infrared (IR) and Submillimeter/Millimeter (sub‐mm/mm) regimes. The scattering/absorption/polarization properties from the precalculated database are incorporated into a radiative transfer model, the Atmospheric Radiative Transfer Model (ARTS). A number of sub‐mm/mm bands have been considered from radiometric and polarimetric studies for potential applications to retrieving ice cloud properties at shorter sub‐mm/mm wavelengths (higher frequencies). The IR atmospheric window has also been thoroughly investigated for its feasibility to observe ice clouds. However, in the literature there is a lack of studies utilizing these high frequency bands or polarimetric IR observations to infer ice cloud properties. This study focuses on performing simulations of ice clouds between 680 and 183 GHz (sub‐mm/mm regime) and between 8.6 and 12 μm wavelengths (IR‐window regime) toward determining potential applications to simultaneous retrievals of ice water path (IWP) and effective diameter (Deff) with a primary focus on optically thin cirrus clouds. IWP and Deff isoline plots (lookup tables) are created to conduct sensitivity analyses and to perform preliminary retrievals. Results reveal that the utilization of both sub‐mm/mm and IR wavelengths combined with linearly polarized and unpolarized measurements leads to the most accurate IWP and Deff retrievals with errors lower than 10%.

A Modified Ice Water Path Retrieval Algorithm Applicable to the ATMS

The algorithm to retrieve the ice water path (IWP) from Microwave Humidity Sounder (MHS) measurements of two window channels at 89 and 157 GHz was already developed, and the IWP retrieval products have been made operationally available since NOAA-15. An adaptation of the same algorithm to the Advanced Technology Microwave Sounder (ATMS) is not straightforward due to differences of central frequencies and fields-of-view (FOVs) of the window channels between ATMS and MHS. In this study, ATMS and MHS orbit data from May 2016 to April 2017 are first collocated using the Simultaneous Nadir Overpass (SNO) method. Then two linear relationships between ATMS and MHS window channels are established using the SNO data: one between MHS channel 1 (89.0 GHz, 15-km nadir resolution) and ATMS channel 16 (88.2 GHz, 32-km nadir resolution), and the other between MHS channel 2 (157.0 GHz, 15-km nadir resolution) and ATMS channel 17 (165.5 GHz, 16-km nadir resolution). Since MHS IWPs are calculated differently when the scattering parameters are small (<0.15 or <0.18) or large (0.15 and 0.18), the histogram of IWP is found to have an unrealistic local minimum around 0.4 kg m−2. A modification is made to use the same algorithm as MHS when the scattering parameter at 89 GHz <0.08 or >0.19, and a linear interpolation within the interval of . The ATMS IWP is then derived using the modified algorithm by obtaining equivalent MHS brightness temperatures according to the regression relationships, while the MHS IWP is derived directly using the modified algorithm. Also, the ATMS results are collocated with MHS orbits for comparison purposes. The spatial distributions of ATMS- and MHS-derived IWPs are consistent. A quantitative analysis shows that the two results are approximately same with deviations smaller than about 0.02 kg m−2.

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

Abstract. Cirrus clouds remain one of the key uncertainties in atmospheric research. To better understand the properties and physical processes of cirrus clouds, accurate large-scale observations from satellites are required. Artificial neural networks (ANNs) have proved to be a useful tool for cirrus cloud remote sensing. Since physics is not modelled explicitly in ANNs, a thorough characterisation of the networks is necessary. In this paper the CiPS (Cirrus Properties from SEVIRI) algorithm is characterised using the space-borne lidar CALIOP. CiPS is composed of a set of ANNs for the cirrus cloud detection, opacity identification and the corresponding cloud top height, ice optical thickness and ice water path retrieval from the imager SEVIRI aboard the geostationary Meteosat Second Generation satellites. First, the retrieval accuracy is characterised with respect to different land surface types. The retrieval works best over water and vegetated surfaces, whereas a surface covered by permanent snow and ice or barren reduces the cirrus detection ability and increases the retrieval errors for the ice optical thickness and ice water path if the cirrus cloud is thin (optical thickness less than approx. 0.3). Second, the retrieval accuracy is characterised with respect to the vertical arrangement of liquid, ice clouds and aerosol layers as derived from CALIOP lidar data. The CiPS retrievals show little interference from liquid water clouds and aerosol layers below an observed cirrus cloud. A liquid water cloud vertically close or adjacent to the cirrus clearly increases the average retrieval errors for the optical thickness and ice water path, respectively, only for thin cirrus clouds with an optical thickness below 0.3 or ice water path below 5.0 g m−2. For the cloud top height retrieval, only aerosol layers affect the retrieval error, with an increased positive bias when the cirrus is at low altitudes. Third, the CiPS retrieval error is characterised with respect to the properties of the investigated cirrus cloud (ice optical thickness and cloud top height). On average CiPS can retrieve the cirrus cloud top height with a relative error around 8 % and no bias and the ice optical thickness with a relative error around 50 % and bias around ±10 % for the most common combinations of cloud top height and ice optical thickness. Similarities with physically based retrieval methods are evident, which implies that even though the retrieval methods differ in the implementation of physics in the model, the retrievals behave similarly due to physical constraints. Finally, we also show that the ANN retrievals have a low sensitivity to radiometric noise in the SEVIRI observations. For optical thickness and ice water path the relative uncertainty due to noise is less than 10 % down to sub-visual cirrus. For the cloud top height retrieval the uncertainty due to noise is around 100 m for all cloud top heights.

Microphysical Properties of Frozen Particles Inferred from Global Precipitation Measurement (GPM) Microwave Imager (GMI) Polarimetric Measurements.

Scattering differences induced by frozen particle microphysical properties are investigated, using the vertically (V) and horizontally (H) polarized radiances from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) 89 and 166 GHz channels. It is the first study on frozen particle microphysical properties on a global scale that uses the dual-frequency microwave polarimetric signals. From the ice cloud scenes identified by the 183.3±3 GHz channel brightness temperature (TB), we find that the scattering by frozen particles is highly polarized with V-H polarimetric differences (PD) being positive throughout the tropics and the winter hemisphere mid-latitude jet regions, including PDs from the GMI 89 and 166 GHz TBs, as well as the PD at 640 GHz from the ER-2 Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) during the TC4 campaign. Large polarization dominantly occurs mostly near convective outflow region (i.e., anvils or stratiform precipitation), while the polarization signal is small inside deep convective cores as well as at the remote cirrus region. Neglecting the polarimetric signal would easily result in as large as 30% error in ice water path retrievals. There is a universal "bell-curve" in the PD - TB relationship, where the PD amplitude peaks at ~ 10 K for all three channels in the tropics and increases slightly with latitude (2-4 K). Moreover, the 166 GHz PD tends to increase in the case where a melting layer is beneath the frozen particles aloft in the atmosphere, while 89 GHz PD is less sensitive than 166 GHz to the melting layer. This property creates a unique PD feature for the identification of the melting layer and stratiform rain with passive sensors. Horizontally oriented non-spherical frozen particles are thought to produce the observed PD because of different ice scattering properties in the V and H polarizations. On the other hand, turbulent mixing within deep convective cores inevitably promotes the random orientation of these particles, a mechanism works effectively on reducing the PD. The current GMI polarimetric measurements themselves cannot fully disentangle the possible mechanisms.

Retrieval of cloud ice water path using SAPHIR on board Megha-Tropiques over the tropical ocean

The SAPHIR sensor onboard Megha-Tropiques (MT) measures the earth emitted radiation at frequencies near the water vapor absorption band. SAPHIR operates in six frequencies ranging from 183±0.1 to 183±11GHz. These frequencies have been used to retrieve cloud ice water path (IWP) at a very high resolution. A method to retrieve IWP over the Indian ocean region is attempted in this study. The study is in two parts, in first part a radiative transfer based simulation is carried out to give an insight of using SAPHIR frequency channels for IWP retrieval, in the next part the real observations of SAPHIR and TRMM-TMI was used for IWP retrieval. The concurrent observations of SAPHIR brightness temperatures (Tbs) and TRMM TMI IWP were used in the development of the retrieval algorithm. An Eigen Vector analysis was done to identify weight of each channel in retrieving IWP; following this a two channel regression based algorithm was developed. The SAPHIR channels which are away from the water vapor absorption band were used to avoid possible water vapor contamination. When the retrieval is compared with independent test dataset, it gives a correlation of 0.80 and RMSE of 3.5%. SAPHIR derived IWP has been compared with other available global IWP products such as TMI, MSPPS, CloudSat and GPM-GMI qualitatively as well as quantitatively. PDF comparison of SAPHIR derived IWP found to have good agreement with CloudSat. Zonal mean comparison with recently launched GMI shows the strength of this algorithm.

The Use of CloudSat Data to Evaluate Retrievals of Total Ice Content in Precipitating Cloud Systems from Ground-Based Operational Radar Measurements

AbstractAn approach is described to retrieve the total amount of ice in a vertical atmospheric column in precipitating clouds observed by the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) systems. This amount expressed as ice water path (IWP) is retrieved using measurements obtained during standard WSR-88D scanning procedures performed when observing precipitation. WSR-88D-based IWP estimates are evaluated using dedicated cloud microphysical retrievals available from the CloudSat and auxiliary spaceborne measurements. The evaluation is performed using measurements obtained in extensive predominantly stratiform precipitation systems containing both ice hydrometeors aloft and rain near the ground. The analysis is based on retrievals of IWP from satellite and the ground-based KWGX and KSHV WSR-88D that are closely collocated in time and space. The comparison results indicate a relatively high correlation between satellite and WSR-88D IWP retrievals, with corresponding correlation coefficients of around 0.7. The mean relative differences between spaceborne and ground-based estimates are around 50%–60%, which is on the order of IWP retrieval uncertainties and is comparable to the differences among various operational CloudSat IWP products. The analysis performed in this study suggests that the quantitative information on ice content of precipitation systems can generally be obtained from operational WSR-88D measurements, when they perform routine scans to observe precipitation. The limitations of WSR-88D IWP estimates due to radar beam tilt restrictions and the overshooting effects due to Earth’s sphericity are discussed.

Synergistic multi-sensor and multi-frequency retrieval of cloud ice water path constrained by CloudSat collocations

The cloud ice water path (IWP) is one of the major parameters that have a strong influence on earth׳s radiation budget. Onboard satellite sensors are recognized as valuable tools to measure the IWP in a global scale. Albeit, active sensors such as the Cloud Profiling Radar (CPR) onboard the CloudSat satellite has better capability to measure the ice water content profile, thus, its vertical integral, IWP, than any passive microwave (MW) or infrared (IR) sensors. In this study, we investigate the retrieval of IWP from MW and IR sensors, including AMSU-A, MHS, and HIRS instruments on-board the N19 satellite, such that the retrieval is consistent with the CloudSat IWP estimates. This is achieved through the collocations between the passive satellite measurements and CloudSat scenes. Potential benefit of synergistic multi-sensor multi-frequency retrieval is investigated. Two modeling approaches are explored for the IWP retrieval – generalized linear model (GLM) and neural network (NN). The investigation has been carried out over both ocean and land surface types. The MW/IR synergy is found to be retrieved more accurate IWP than the individual AMSU-A, MHS, or HIRS measurements. Both GLM and NN approaches have been able to exploit the synergistic retrievals.

Estimating Ice Water Path in Tropical Cyclones With Multispectral Microwave Data From the FY-3B Satellite

Ice water path (IWP) is an important parameter to characterize tropical cyclones. The FY-3B satellite, with multiple passive microwave sensors onboard, offers a unique opportunity to monitor the variation of cloud IWP during the evolution of tropical cyclones. In this paper, by using the combined simultaneous measurements of the MicroWave atmospheric Humidity Sounder and MicroWave Radiometer Imager on FY-3B satellite, an improved IWP algorithm for tropical clouds is developed. The new algorithm seeks to better estimate ice-free background brightness temperature at 150 GHz using simultaneous observations at low microwave frequencies. This approach improves IWP retrieval accuracy particularly for high IWP clouds that often associated with tropical cyclones. The current algorithm was applied to FY-3B observations of two typhoons with different strengths, and the characteristics of IWP variation at the storms' different evolution stages were investigated. The results showed that IWP tends to vary ahead of the storm intensification or decay, suggesting that IWP can be potentially used to predict the change of storm intensity.

CloudSat-constrained cloud ice water path and cloud top height retrievals from MHS 157 and 183.3 GHz radiances

Abstract. Ice water path (IWP) and cloud top height (ht) are two of the key variables in determining cloud radiative and thermodynamical properties in climate models. Large uncertainty remains among IWP measurements from satellite sensors, in large part due to the assumptions made for cloud microphysics in these retrievals. In this study, we develop a fast algorithm to retrieve IWP from the 157, 183.3 ± 3 and 190.3 GHz radiances of the Microwave Humidity Sounder (MHS) such that the MHS cloud ice retrieval is consistent with CloudSat IWP measurements. This retrieval is obtained by constraining the empirical forward models between collocated and coincident measurements of CloudSat IWP and MHS cloud-induced radiance depression (Tcir) at these channels. The empirical forward model is represented by a look-up table (LUT) of Tcir–IWP relationships as a function of ht and the frequency channel. With ht simultaneously retrieved, the IWP is found to be more accurate. The useful range of the MHS IWP retrieval is between 0.5 and 10 kg m−2, and agrees well with CloudSat in terms of the normalized probability density function (PDF). Compared to the empirical model, current operational radiative transfer models (RTMs) still have significant uncertainties in characterizing the observed Tcir–IWP relationships. Therefore, the empirical LUT method developed here remains an effective approach to retrieving ice cloud properties from the MHS-like microwave channels.

Systematic and random errors between collocated satellite ice water path observations

AbstractThere remains large disagreement between ice‐water path (IWP) in observational data sets, largely because the sensors observe different parts of the ice particle size distribution. A detailed comparison of retrieved IWP from satellite observations in the Tropics (±30° latitude) in 2007 was made using collocated measurements. The radio detection and ranging(radar)/light detection and ranging (lidar) (DARDAR) IWP data set, based on combined radar/lidar measurements, is used as a reference because it provides arguably the best estimate of the total column IWP. For each data set, usable IWP dynamic ranges are inferred from this comparison. IWP retrievals based on solar reflectance measurements, in the moderate resolution imaging spectroradiometer (MODIS), advanced very high resolution radiometer–based Climate Monitoring Satellite Applications Facility (CMSAF), and Pathfinder Atmospheres‐Extended (PATMOS‐x) datasets, were found to be correlated with DARDAR over a large IWP range (~20–7000 g m‐2). The random errors of the collocated data sets have a close to lognormal distribution, and the combined random error of MODIS and DARDAR is less than a factor of 2, which also sets the upper limit for MODIS alone. In the same way, the upper limit for the random error of all considered data sets is determined. Data sets based on passive microwave measurements, microwave surface and precipitation products system (MSPPS), microwave integrated retrieval system (MiRS), and collocated microwave only (CMO), are largely correlated with DARDAR for IWP values larger than approximately 700 g m‐2. The combined uncertainty between these data sets and DARDAR in this range is slightly less MODIS‐DARDAR, but the systematic bias is nearly an order of magnitude.

Effects of ice particle size vertical inhomogeneity on the passive remote sensing of ice clouds

The solar reflectance bi‐spectral (SRBS) and infrared split‐window (IRSpW) methods are two of the most popular techniques for passive ice cloud property retrievals from multispectral imagers. Ice clouds are usually assumed to be vertically homogeneous in global operational algorithms based on these methods, although significant vertical variations of ice particle size are typically observed in ice clouds. In this study we investigate uncertainties in retrieved optical thickness, effective particle size, and ice water path introduced by a homogeneous cloud assumption in both the SRBS and IRSpW methods, and focus on whether the assumption can lead to significant discrepancies between the two methods. The study simulates the upwelling spectral radiance associated with vertically structured clouds and passes the results through representative SRBS and IRSpW retrieval algorithms. Cloud optical thickness is limited to values for which IRSpW retrievals are possible (optical thickness less than about 7). When the ice cloud is optically thin and yet has a significant ice particle size vertical variation, it is found that both methods tend to underestimate the effective radius and ice water path. The reason for the underestimation is the nonlinear dependence of ice particle scattering properties (extinction and single scattering albedo) on the effective radius. Because the nonlinearity effect is stronger in the IRSpW than the SRBS method, the IRSpW‐based IWP tends to be smaller than the SRBS counterpart. When the ice cloud is moderately optically thick, the IRSpW method is relatively insensitive to cloud vertical structure and effective radius retrieval is weighted toward smaller ice particle size, while the weighting function makes the SRBS method more sensitive to the ice particle size in the upper portion of the cloud. As a result, when ice particle size increases monotonically toward cloud base, the two methods are in qualitative agreement; in the event that ice particle size decreases toward cloud base, the effective radius and ice water path retrievals based on the SRBS method are substantially larger than those from the IRSpW. The main findings of this study suggest that the homogenous cloud assumption can affect the SRBS and IRSpW methods to different extents and, consequently, can lead to significantly different retrievals. Therefore caution should be taken when comparing and combining the ice cloud property retrievals from these two methods.

A Method to Estimate Vertically Integrated Amounts of Cloud Ice and Liquid and Mean Rain Rate in Stratiform Precipitation from Radar and Auxiliary Data

Abstract A method to retrieve total vertical amounts of cloud liquid and ice in stratiform precipitating systems is described. The retrievals use measurements from the vertically pointing Ka- and W-band cloud radars operated by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program and auxiliary measurements from a scanning National Weather Service radar and a ground-based disdrometer. Separation between the cloud liquid and rain is based on estimations of the total attenuation of millimeter-wavelength radar signals in the liquid hydrometeor layer. Disdrometer measurements are used for the retrieval constraints. Because the liquid phase hydrometeor retrievals use only differential measurements, they are immune to the absolute radar calibration uncertainties. Estimates of the ice cloud phase are performed using empirical relations between absolute radar reflectivity and ice water content. Data from the nearby scanning weather-service radar, which operates at a lower frequency, are used to correct cloud radar measurements observed above the freezing level for attenuation caused by the layers of liquid and melting hydrometeors and also by wet radomes of cloud radars. Polarimetric and vertical Doppler measurements from ARM cloud radars provide a distinct separation between regions of liquid and ice phases, and therefore the corresponding retrievals are performed in each region separately. The applicability of the suggested method is illustrated for a stratiform precipitation event observed at the ARM Southern Great Plains facility. Expected uncertainties for retrievals of cloud liquid water path are estimated at about 200–250 g m−2 for typical rainfall rates observed in stratiform systems (∼3–4 mm h−1). These uncertainties increase as rainfall rate increases. The ice water path retrieval uncertainties can be as high as a factor of 2.

A Method to Estimate Vertically Integrated Amounts of Cloud Ice and Liquid and Mean Rain Rate in Stratiform Precipitation from Radar and Auxiliary Data

A method to retrieve total vertical amounts of cloud liquid and ice in stratiform precipitating systems is described. The retrievals use measurements from the vertically pointing Ka- and W-band cloud radars operated by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Program and auxiliary measurements from a scanning National Weather Service radar and a ground-based disdrometer. Separation between the cloud liquid and rain is based on estimations of the total attenuation of millimeter-wavelength radar signals in the liquid hydrometeor layer. Disdrometer measurements are used for the retrieval constraints. Because the liquid phase hydrometeor retrievals use only differential measurements, they are immune to the absolute radar calibration uncertainties. Estimates of the ice cloud phase are performed using empirical relations between absolute radar reflectivity and ice water content. Data from the nearby scanning weather-service radar, which operates at a lower frequency, are used to correct cloud radar measurements observed above the freezing level for attenuation caused by the layers of liquid and melting hydrometeors and also by wet radomes of cloud radars. Polarimetric and vertical Doppler measurements from ARM cloud radars provide a distinct separation between regions of liquid and ice phases, and therefore the corresponding retrievals are performed in each region separately. The applicability of the suggested method is illustrated for a stratiform precipitation event observed at the ARM Southern Great Plains facility. Expected uncertainties for retrievals of cloud liquid water path are estimated at about 200–250 g m−2 for typical rainfall rates observed in stratiform systems (∼3–4 mm h−1). These uncertainties increase as rainfall rate increases. The ice water path retrieval uncertainties can be as high as a factor of 2.