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An Optimal Estimation Retrieval Algorithm for Microwave Humidity Sounding Channels with Minimal Scan Position Biasmagnitude of changes that are possible over the size of the instrument FOV. Both imagers and sounders have been used for several decades to estimate TPW over the global oceans, and in recent years many retrieval algorithms have been put forth (e.g., Boukabara et al. 2010 ; Duncan and Kummerow 2016 , hereafter DK16 ; Mears et al. 2015 ) that agree reasonably well with observations from radiosondes and ground-based measurements from global positioning system (GPS) stations. These algorithms, however
magnitude of changes that are possible over the size of the instrument FOV. Both imagers and sounders have been used for several decades to estimate TPW over the global oceans, and in recent years many retrieval algorithms have been put forth (e.g., Boukabara et al. 2010 ; Duncan and Kummerow 2016 , hereafter DK16 ; Mears et al. 2015 ) that agree reasonably well with observations from radiosondes and ground-based measurements from global positioning system (GPS) stations. These algorithms, however
A Passive Microwave Retrieval Algorithm with Minimal View-Angle Bias: Application to the TEMPEST-D CubeSat Missionparticular, using yaw maneuvers performed by the TEMPEST-D spacecraft, we can assess view-angle biases in a novel way, by comparing retrievals made by the same instrument at nearly the same time and over nearly the same area, but from different view angles. In this paper we apply the CSU 1DVAR retrieval algorithm ( Schulte and Kummerow 2019 ; hereinafter SK19 ) to TEMPEST-D observations to retrieve total precipitable water (TPW), cloud liquid water path (LWP), and cloud ice water path (IWP) to answer
particular, using yaw maneuvers performed by the TEMPEST-D spacecraft, we can assess view-angle biases in a novel way, by comparing retrievals made by the same instrument at nearly the same time and over nearly the same area, but from different view angles. In this paper we apply the CSU 1DVAR retrieval algorithm ( Schulte and Kummerow 2019 ; hereinafter SK19 ) to TEMPEST-D observations to retrieve total precipitable water (TPW), cloud liquid water path (LWP), and cloud ice water path (IWP) to answer
Algorithm for Determining the Statistical Properties of Cloud Particles through In Situ Ensemble Measurementsas the Forward Scattering Spectrometer Probe (FSSP; PMS Inc.) and the Cloud Droplet Probe (CDP; Droplet Measurement Technology), the sample volume is constrained to minimize the probability of two particles in the same measurement volume. Considerable effort has been directed at avoiding coincident particles in such probes, including the use of qualifier channels to identify data points where multiple particles are in the sample volume and development of correction algorithms (e.g., Cooper 1988
as the Forward Scattering Spectrometer Probe (FSSP; PMS Inc.) and the Cloud Droplet Probe (CDP; Droplet Measurement Technology), the sample volume is constrained to minimize the probability of two particles in the same measurement volume. Considerable effort has been directed at avoiding coincident particles in such probes, including the use of qualifier channels to identify data points where multiple particles are in the sample volume and development of correction algorithms (e.g., Cooper 1988
(2002) argue that the redundancy among Z H , Z DR , and K DP precludes the retrieval of all three DSD parameters in Eq. (1) with this parameter set. Bringi et al. (2002) applied the method only to situations in which K DP ≥ 0.3° km −1 (rain rates >20 mm h −1 ). Hence, the technique has limited application for general DSD retrievals. In section 2 we provide background information on radar observations and a brief review of the attenuation correction algorithm. Section 3 gives an
(2002) argue that the redundancy among Z H , Z DR , and K DP precludes the retrieval of all three DSD parameters in Eq. (1) with this parameter set. Bringi et al. (2002) applied the method only to situations in which K DP ≥ 0.3° km −1 (rain rates >20 mm h −1 ). Hence, the technique has limited application for general DSD retrievals. In section 2 we provide background information on radar observations and a brief review of the attenuation correction algorithm. Section 3 gives an
polarization plane of the outgoing beam. Each component is measured separately using photomultiplier tubes (PMTs). Additional information about the CALIOP transmitter and receiver design and operation can be found in Hunt et al. (2009) . Accurate calibration of all three lidar signals is essential for layer detection and the subsequent retrieval of layer optical properties. Complete details of all CALIOP calibration algorithms are presented in Hostetler et al. (2006) . Essential aspects of the CALIOP
polarization plane of the outgoing beam. Each component is measured separately using photomultiplier tubes (PMTs). Additional information about the CALIOP transmitter and receiver design and operation can be found in Hunt et al. (2009) . Accurate calibration of all three lidar signals is essential for layer detection and the subsequent retrieval of layer optical properties. Complete details of all CALIOP calibration algorithms are presented in Hostetler et al. (2006) . Essential aspects of the CALIOP
model analysis fields for their predictor data. Thus, in a way, they are at the mercy of the model’s data and assimilation scheme. The algorithm described here was developed to produce a high-quality TCC dataset that can, among other things, be used to improve tropical cyclogenesis forecasts. The algorithm uses a global Gridded Satellite (GridSat) dataset (see section 2a ) that has been calibrated to produce consistent global IR brightness temperatures ( T b ) over a 30-yr period (1980–2009); the
model analysis fields for their predictor data. Thus, in a way, they are at the mercy of the model’s data and assimilation scheme. The algorithm described here was developed to produce a high-quality TCC dataset that can, among other things, be used to improve tropical cyclogenesis forecasts. The algorithm uses a global Gridded Satellite (GridSat) dataset (see section 2a ) that has been calibrated to produce consistent global IR brightness temperatures ( T b ) over a 30-yr period (1980–2009); the
different altitudes. Referred to as the multiaircraft flight (MAF) scheme, it forms the basis of the study reported in the present paper. However, the retrieval algorithm of cloud tomography is much more sophisticated and time consuming than in situ measurements and active remote sensing. Zhou et al. (2011) pointed out that a rise in total LWC makes the nonlinearity of the objective function stronger. Linearization of the objective function would lead to model errors that slow down the convergent rate
different altitudes. Referred to as the multiaircraft flight (MAF) scheme, it forms the basis of the study reported in the present paper. However, the retrieval algorithm of cloud tomography is much more sophisticated and time consuming than in situ measurements and active remote sensing. Zhou et al. (2011) pointed out that a rise in total LWC makes the nonlinearity of the objective function stronger. Linearization of the objective function would lead to model errors that slow down the convergent rate
grids is correlated to the amount of solar irradiance ( Girodo et al. 2006 ; McGranaghan et al. 2008 ). Very short-range (i.e., up to 6-h) sky condition forecasts are useful to decision makers for these applications. In this paper, we present findings from the development and testing of six advanced, observation (obs)-based prediction algorithms designed to forecast clear-sky condition. These methods have their heritage in a number of technical disciplines including statistics, applied mathematics
grids is correlated to the amount of solar irradiance ( Girodo et al. 2006 ; McGranaghan et al. 2008 ). Very short-range (i.e., up to 6-h) sky condition forecasts are useful to decision makers for these applications. In this paper, we present findings from the development and testing of six advanced, observation (obs)-based prediction algorithms designed to forecast clear-sky condition. These methods have their heritage in a number of technical disciplines including statistics, applied mathematics
Toward an Algorithm for Estimating Latent Heat Release in Warm Rain Systems) simulations to retrieve latent heating using rain rate—a proxy for cloud depth—and cloud type. The hydrometeor heating product ( Yang and Smith 1999 ), on the other hand, uses features of vertical velocity, rain rate, and the hydrometeor profile as input into a CRM to calculate latent heating. Yet another approach is the trained radiometer (TRAIN) algorithm ( Olson et al. 1999 ), which utilizes a trained database of reflectivity and brightness temperature composites to estimate latent heating from passive
) simulations to retrieve latent heating using rain rate—a proxy for cloud depth—and cloud type. The hydrometeor heating product ( Yang and Smith 1999 ), on the other hand, uses features of vertical velocity, rain rate, and the hydrometeor profile as input into a CRM to calculate latent heating. Yet another approach is the trained radiometer (TRAIN) algorithm ( Olson et al. 1999 ), which utilizes a trained database of reflectivity and brightness temperature composites to estimate latent heating from passive
1. Introduction The recent release of version 7 (V7) of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) 2A25 and Microwave Imager (TMI) 2A12 rainfall algorithms presents an opportunity to compare the new version (V7) to the previous release, version 6 (V6). It is commonly accepted that these algorithms perform well on large spatial and temporal scales, but they often have more significant local errors that are problematic on a case-by-case basis. These errors tend to be
1. Introduction The recent release of version 7 (V7) of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) 2A25 and Microwave Imager (TMI) 2A12 rainfall algorithms presents an opportunity to compare the new version (V7) to the previous release, version 6 (V6). It is commonly accepted that these algorithms perform well on large spatial and temporal scales, but they often have more significant local errors that are problematic on a case-by-case basis. These errors tend to be
