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F. Joseph Turk
,
Ziad S. Haddad
, and
Yalei You

Abstract

The upcoming Global Precipitation Measurement mission will provide considerably more overland observations over complex terrain, high-elevation river basins, and cold surfaces, necessitating an improved assessment of the microwave land surface emissivity. Current passive microwave overland rainfall algorithms developed for the Tropical Rainfall Measuring Mission (TRMM) rely upon hydrometeor scattering-induced signatures at high-frequency (85 GHz) brightness temperatures (TBs) and are empirical in nature. A multiyear global database of microwave surface emissivities encompassing a wide range of surface conditions was retrieved from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) radiometric clear scenes using companion A-Train [CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Atmospheric Infrared Sounder (AIRS)] data. To account for the correlated emissivity structure, the procedure first derives the TRMM Microwave Imager–like nine-channel emissivity principal component (PC) structure. Relations are derived to estimate the emissivity PCs directly from the instantaneous TBs, which allows subsequent TB observations to estimate the PC structure and reconstruct the emissivity vector without need for ancillary data regarding the surface or atmospheric conditions. Radiative transfer simulations matched the AMSR-E TBs within 5–7-K RMS difference in the absence of precipitation. Since the relations are derived specifically for clear-scene conditions, discriminant analysis was performed to find the PC discriminant that best separates clear and precipitation scenes. When this technique is applied independently to two years of TRMM data, the PC-based discriminant demonstrated superior relative operating characteristics relative to the established 85-GHz scattering index, most notably during cold seasons.

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Stephen R. Guimond
,
Gerald M. Heymsfield
, and
F. Joseph Turk

Abstract

A synthesis of remote sensing and in situ observations throughout the life cycle of Hurricane Dennis (2005) during the NASA Tropical Cloud Systems and Processes (TCSP) experiment is presented. Measurements from the ER-2 Doppler radar (EDOP), the Advanced Microwave Sounding Unit (AMSU), airborne radiometer, and flight-level instruments are used to provide a multiscale examination of the storm. The main focus is an episode of deep convective bursts (“hot towers”) occurring during a mature stage of the storm and preceding a period of rapid intensification (11-hPa pressure drop in 1 h 35 min). The vigorous hot towers penetrated to 16-km height, had maximum updrafts of 20 m s−1 at 12–14-km height, and possessed a strong transverse circulation through the core of the convection. Significant downdrafts (maximum of 10–12 m s−1) on the flanks of the updrafts were observed, with their cumulative effects hypothesized to result in the observed increases in the warm core. In one ER-2 overpass, subsidence was transported toward the eye by 15–20 m s−1 inflow occurring over a deep layer (0.5–10 km) coincident with a hot tower.

Fourier analysis of the AMSU satellite measurements revealed a large shift in the storm’s warm core structure, from asymmetric to axisymmetric, ∼12 h after the convective bursts began. In addition, flight-level wind calculations of the axisymmetric tangential velocity and inertial stability showed a contraction of the maximum winds and an increase in the stiffness of the vortex, respectively, after the EDOP observations.

The multiscale observations presented here reveal unique, ultra-high-resolution details of hot towers and their coupling to the parent vortex, the balanced dynamics of which can be generally explained by the axisymmetrization and efficiency theories.

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F. Joseph Turk
,
R. Sikhakolli
,
P. Kirstetter
, and
S. L. Durden

Abstract

Estimation of overland precipitation using observations from the radar and passive microwave radiometer sensors onboard the current Global Precipitation Measurement (GPM) and predecessor Tropical Rainfall Measuring Mission (TRMM) satellites is constrained by the underlying surface variability. The factors controlling the multichannel microwave surface emissivity and radar surface backscatter are related to surface properties such as soil type and vegetation properties that vary with location and time. Variability due to slowly varying seasonal changes can be considered when simulating radar reflectivities and radiometer equivalent blackbody brightness temperatures for use with precipitation retrieval algorithms. However, over certain surfaces, a more transient, dynamic surface change is manifested upon the onset of intermittent rain events. In these situations, a timely update of the surface state prior to each satellite overpass, together with knowledge of the associated variability in the emissivity and radar surface backscatter, may be useful to improve the performance of the overland precipitation retrieval algorithms. In this study, the potential for wide-swath surface backscatter observations from the Ku-band, dual-beam OceanSat-2 scatterometer (OSCAT) is examined as a surface reference for tracking previous-time precipitation. Over certain surfaces, it is shown that a time-change detection approach is useful to isolate the change in radar backscatter owing to previous 3-h rainfall accumulations from the more slowly varying background state. A practical use of this method would be the production of an ancillary previous-time precipitation map, which could be consulted by retrieval algorithms to select (or adjust the weighting of) candidate solutions that represent the most current surface conditions.

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K. Franklin Evans
,
Joseph Turk
,
Takmeng Wong
, and
Graeme L. Stephens

Abstract

A multichannel passive microwave precipitation retrieval algorithm is developed. Bayes theorem is used to combine statistical information from numerical cloud models with forward radiative transfer modeling. Amultivariate lognormal prior probability distribution contains the covariance information about hydrometeor distributions that resolves the nonuniqueness inherent in the inversion process. Hydrometeor profiles are retrieved by maximizing the posterior probability density for each vector of observations. The hydrometeor profile retrievalmethod is tested with data from the Advanced Microwave Precipitation Radiometer (IO, 19, 37, and 85 GHz) of convection over ocean and land in Florida. The CP-2 multiparameter radar data are used to verify theretrieved profiles. The results show that the method can retrieve approximate hydrometeor profiles, with larger errors over land than water. There is considerably greater accuracy in the retrieval of integrated hydrometeor contents than of profiles. Many of the retrieval errors are traced to problems with the cloud model microphysicalinformation, and future improvements to the algorithm are suggested.

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William S. Olson
,
Ye Hong
,
Christian D. Kummerow
, and
Joseph Turk

Abstract

Observational and modeling studies have revealed the relationships between convective–stratiform rain proportion and the vertical distributions of vertical motion, latent heating, and moistening in mesoscale convective systems. Therefore, remote sensing techniques that can be used to quantify the area coverage of convective or stratiform rainfall could provide useful information regarding the dynamic and thermodynamic processes in these systems. In the current study, two methods for deducing the area coverage of convective precipitation from satellite passive microwave radiometer measurements are combined to yield an improved method. If sufficient microwave scattering by ice-phase precipitation is detected, the method relies mainly on the degree of polarization in oblique-view, 85.5-GHz radiances to estimate the fraction of the radiometer footprint covered by convection. In situations where ice scattering is minimal, the method draws mostly on texture information in radiometer imagery at lower microwave frequencies to estimate the convective area fraction.

Based upon observations of 10 organized convective systems over ocean and nine systems over land, instantaneous, 0.5°-resolution estimates of convective area fraction from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are compared with nearly coincident estimates from the TRMM precipitation radar (PR). TMI convective area fraction estimates are low-biased relative to PR estimates, with TMI–PR correlation coefficients of 0.78 and 0.84 over ocean and land surfaces, respectively. TMI monthly average convective area percentages in the Tropics and subtropics from February 1998 are greatest along the intertropical convergence zone and in the continental regions of the Southern (summer) Hemisphere. Although convective area percentages from the TMI are systematically lower than those derived from the PR, the monthly patterns of convective coverage are similar. Systematic differences in TMI and PR convective area percentages do not show any clear correlation or anticorrelation with differences in retrieved rain depths, and so discrepancies between TRMM version-5 TMI- and PR-retrieved rain depths are likely due to other sensor or algorithmic differences.

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Thomas F. Lee
,
F. Joseph Turk
, and
Kim Richardson

Abstract

Using data from the GOES-8–9 imager, this paper discusses the potential for consistent, around-the-clock image products that can trace the movement and evolution of low, stratiform clouds. In particular, the paper discusses how bispectral image sequences based on the shortwave (3.9 μm) and longwave (10.7 μm) infrared channels can be developed for this purpose. These sequences can be animated to produce useful loops. The techniques address several problems faced by operational forecasters in the tracking of low clouds. Low clouds are often difficult or impossible to detect at night because of the poor thermal contrast with the background on infrared images. During the day, although solar reflection makes low, stratiform clouds bright on GOES visible images, it is difficult to distinguish low clouds from adjacent ground snowcover or dense cirrus overcasts. The shortwave infrared channel often gives a superior delineation of low clouds on images because water droplets produce much higher reflectances than ice clouds or ground snowcover. Combined with the longwave channel, the shortwave channel can be used to derive products that can distinguish low clouds from the background at any time of day or night. The first case study discusses cloud properties as observed from the shortwave channels from the polar-orbiting Advanced Very High Resolution Radiometer, as well as GOES-9, and applies a correction to produce shortwave reflectance. A second case study illustrates the use of the GOES-8 shortwave channel to observe the aftermath of a spring snowstorm in the Ohio Valley. Finally, the paper discusses a red–blue–green color combination technique to build useful forecaster products.

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Nobuyuki Utsumi
,
Hyungjun Kim
,
F. Joseph Turk
, and
Ziad. S. Haddad

Abstract

Quantifying time-averaged rain rate, or rain accumulation, on subhourly time scales is essential for various application studies requiring rain estimates. This study proposes a novel idea to estimate subhourly time-averaged surface rain rate based on the instantaneous vertical rain profile observed from low-Earth-orbiting satellites. Instantaneous rain estimates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) are compared with 1-min surface rain gauges in North America and Kwajalein atoll for the warm seasons of 2005–14. Time-lagged correlation analysis between PR rain rates at various height levels and surface rain gauge data shows that the peak of the correlations tends to be delayed for PR rain at higher levels up to around 6-km altitude. PR estimates for low to middle height levels have better correlations with time-delayed surface gauge data than the PR’s estimated surface rain rate product. This implies that rain estimates for lower to middle heights may have skill to estimate the eventual surface rain rate that occurs 1–30 min later. Therefore, in this study, the vertical profiles of TRMM PR instantaneous rain estimates are averaged between the surface and various heights above the surface to represent time-averaged surface rain rate. It was shown that vertically averaged PR estimates up to middle heights (~4.5 km) exhibit better skill, compared to the PR estimated instantaneous surface rain product, to represent subhourly (~30 min) time-averaged surface rain rate. These findings highlight the merit of additional consideration of vertical rain profiles, not only instantaneous surface rain rate, to improve subhourly surface estimates of satellite-based rain products.

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F. Joseph Turk
,
Z. S. Haddad
, and
Y. You

Abstract

The joint National Aeronautics and Space Administration (NASA) and Japanese Aerospace Exploration Agency (JAXA) Global Precipitation Measurement (GPM) is a constellation mission, centered upon observations from the core satellite dual-frequency precipitation radar (DPR) and its companion passive microwave (MW) GPM Microwave Imager (GMI). One of the key challenges for GPM is how to link the information from the single DPR across all passive MW sensors in the constellation, to produce a globally consistent precipitation product. Commonly, the associated surface emissivity and environmental conditions at the satellite observation time are interpolated from ancillary data, such as global forecast models and emissivity climatology, and are used for radiative transfer simulations and cataloging/indexing the brightness temperature (TB) observations and simulations within a common MW precipitation retrieval framework.

In this manuscript, the feasibility of an update to the surface emissivity state at or near the satellite observation time, regardless of surface type, is examined for purposes of assisting these algorithms with specification of the surface and environmental conditions. Since the constellation MW radiometers routinely observe many more nonprecipitating conditions than precipitating conditions, a principal component analysis is developed from the noncloud GMI–DPR observations as a means to characterize the emissivity state vector and to consistently track the surface and environmental conditions. The method is demonstrated and applied over known complex surface conditions to probabilistically separate cloud and cloud-free scenes. The ability of the method to globally identify “self-similar” surface locations from the TB observations without requiring any ancillary knowledge of geographical location or time is demonstrated.

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Nobuyuki Utsumi
,
F. Joseph Turk
,
Ziad S. Haddad
,
Pierre-Emmanuel Kirstetter
, and
Hyungjun Kim

Abstract

Precipitation estimation based on passive microwave (MW) observations from low-Earth-orbiting satellites is one of the essential variables for understanding the global climate. However, almost all validation studies for such precipitation estimation have focused only on the surface precipitation rate. This study investigates the vertical precipitation profiles estimated by two passive MW-based retrieval algorithms, i.e., the emissivity principal components (EPC) algorithm and the Goddard profiling algorithm (GPROF). The passive MW-based condensed water content profiles estimated from the Global Precipitation Measurement Microwave Imager (GMI) are validated using the GMI + Dual-Frequency Precipitation Radar combined algorithm as the reference product. It is shown that the EPC generally underestimates the magnitude of the condensed water content profiles, described by the mean condensed water content, by about 20%–50% in the middle-to-high latitudes, while GPROF overestimates it by about 20%–50% in the middle-to-high latitudes and more than 50% in the tropics. Part of the EPC magnitude biases is associated with the representation of the precipitation type (i.e., convective and stratiform) in the retrieval algorithm. This suggests that a separate technique for precipitation type identification would aid in mitigating these biases. In contrast to the magnitude of the profile, the profile shapes are relatively well represented by these two passive MW-based retrievals. The joint analysis between the estimation performances of the vertical profiles and surface precipitation rate shows that the physically reasonable connections between the surface precipitation rate and the associated vertical profiles are achieved to some extent by the passive MW-based algorithms.

Open access
Zeinab Takbiri
,
Ardeshir Ebtehaj
,
Efi Foufoula-Georgiou
,
Pierre-Emmanuel Kirstetter
, and
F. Joseph Turk

Abstract

Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth’s cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.

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