SCALE ANALYSIS OF INFRARED WATER VAPOR BRIGHTNESS TEMPERATURES FOR TROPICAL CYCLONE ALL‐SKY RADIANCE ASSIMILATION

Abstract Consistency of upper-tropospheric water vapor measurements from a variety of state-of-the-art instruments was assessed using collocated Geostationary Operational Environmental Satellite-8 (GOES-8) 6.7-μm brightness temperatures as a common benchmark during the Atmospheric Radiation Measurement Program (ARM) First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Water Vapor Experiment (AFWEX). To avoid uncertainties associated with the inversion of satellite-measured radiances into water vapor quantity, profiles of temperature and humidity observed from in situ, ground-based, and airborne instruments are inserted into a radiative transfer model to simulate the brightness temperature that the GOES-8 would have observed under those conditions (i.e., profile-to-radiance approach). Comparisons showed that Vaisala RS80-H radiosondes and Meteolabor Snow White chilled-mirror dewpoint hygrometers are systemically drier in the upper troposphere by ∼30%–40% relative to the GOES-8 measured upper-tropospheric humidity (UTH). By contrast, two ground-based Raman lidars (Cloud and Radiation Test Bed Raman lidar and scanning Raman lidar) and one airborne differential absorption lidar agree to within 10% of the GOES-8 measured UTH. These results indicate that upper-tropospheric water vapor can be monitored by these lidars and well-calibrated, stable geostationary satellites with an uncertainty of less than 10%, and that correction procedures are required to rectify the inherent deficiencies of humidity measurements in the upper troposphere from these radiosondes.

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Progress toward Cloud-Cleared Infrared radiance assimilation in a global modeling framework: Application to the 2017 Atlantic Tropical Cyclone Season.

10.5194/egusphere-egu21-2870 ◽

2021 ◽

Author(s):

Niama Boukachaba ◽

Oreste Reale ◽

Erica L. McGrath-Spangler ◽

Manisha Ganeshan ◽

Will McCarty ◽

...

Keyword(s):

Tropical Cyclone ◽

Strong Impact ◽

Modeling Framework ◽

Atmospheric Infrared Sounder ◽

Clear Sky ◽

Earth Observing System ◽

Infrared Radiance ◽

Radiance Assimilation ◽

The Impact ◽

Tropical Cyclone Season

<p>Previous work by this team has demonstrated that assimilation of IR radiances in partially cloudy regions is beneficial to numerical weather predictions (NWPs), improving the representation of tropical cyclones (TCs) in global analyses and forecasts. The specific technique used by this team is based on the &#8220;cloud-clearing CC&#8221; methodology. Cloud-cleared hyperspectral IR radiances (CCRs), if thinned more aggressively than clear-sky radiances, have shown a strong impact on the analyzed representation and structure of TCs. However, the use of CCRs in an operational context is limited by 1) latency; and 2) external dependencies present in the original cloud-clearing algorithm. In this study, the Atmospheric InfraRed Sounder (AIRS) CC algorithm was (a) ported to NASA high end computing resources (HEC), (b) deprived of external dependencies, and (c) parallelized improving the processing by a factor of 70. The revised AIRS CC algorithm is now customizable, allowing user&#8217;s choice of channel selection, user&#8217;s model's fields as first guess, and could perform in real time. This study examines the benefits achieved when assimilating CCRs using the NASA&#8217;s Goddard Earth Observing System (GEOS) hybrid 4DEnVar system. The focus is on the 2017 Atlantic hurricane season with three infamous hurricanes (Harvey, Irma, and Maria) investigated in depth.&#160; The impact of assimilating customized CCRs on the analyzed representation of tropical cyclone horizontal and vertical structure and on forecast skill is discussed.</p>

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AMSR2 all-sky radiance assimilation and its impact on the analysis and forecast of Hurricane Sandy with a limited-area data assimilation system

Tellus A Dynamic Meteorology and Oceanography ◽

10.3402/tellusa.v68.30917 ◽

2016 ◽

Vol 68 (1) ◽

pp. 30917 ◽

Cited By ~ 27

Author(s):

Chun Yang ◽

Zhiquan Liu ◽

Jamie Bresch ◽

Syed R. H. Rizvi ◽

Xiang-Yu Huang ◽

...

Keyword(s):

Data Assimilation ◽

Hurricane Sandy ◽

Limited Area ◽

Radiance Assimilation ◽

Sky Radiance ◽

Data Assimilation System ◽

Assimilation System ◽

Area Data

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Tropical Cyclone Rain Retrievals from FY-3B MWRI Brightness Temperatures Using the Goddard Profiling Algorithm (GPROF)

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-18-0167.1 ◽

2019 ◽

Vol 36 (5) ◽

pp. 849-864 ◽

Cited By ~ 1

Author(s):

Ruanyu Zhang ◽

Christian D. Kummerow ◽

David L. Randel ◽

Paula J. Brown ◽

Wesley Berg ◽

...

Keyword(s):

Tropical Cyclone ◽

A Priori ◽

Dual Frequency ◽

Ancillary Data ◽

Light Rain ◽

Brightness Temperatures ◽

The Mean ◽

Microwave Imager ◽

Rain Rates ◽

Global Precipitation

AbstractThis study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.

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Normalized Convective Characteristics of Tropical Cyclone Rapid Intensification Events in the North Atlantic and Eastern North Pacific

Monthly Weather Review ◽

10.1175/mwr-d-17-0239.1 ◽

2018 ◽

Vol 146 (4) ◽

pp. 1133-1155 ◽

Cited By ~ 14

Author(s):

Michael S. Fischer ◽

Brian H. Tang ◽

Kristen L. Corbosiero ◽

Christopher M. Rozoff

Keyword(s):

Tropical Cyclone ◽

North Atlantic ◽

North Pacific ◽

Forecast Skill ◽

Passive Microwave ◽

Intensity Change ◽

Rapid Intensification ◽

Brightness Temperatures ◽

The North ◽

The North Atlantic

The relationship between tropical cyclone (TC) convective characteristics and TC intensity change is explored using infrared and passive microwave satellite imagery of TCs in the North Atlantic and eastern North Pacific basins from 1989 to 2016. TC intensity change episodes were placed into one of four groups: rapid intensification (RI), slow intensification (SI), neutral (N), and weakening (W). To account for differences in the distributions of TC intensity among the intensity change groups, a normalization technique is introduced, which allows for the analysis of anomalous TC convective characteristics and their relationship to TC intensity change. A composite analysis of normalized convective parameters shows anomalously cold infrared and 85-GHz brightness temperatures, as well as anomalously warm 37-GHz brightness temperatures, in the upshear quadrants of the TC are associated with increased rates of TC intensification, including RI. For RI episodes in the North Atlantic basin, an increase in anomalous liquid hydrometeor content precedes anomalous ice hydrometeor content by approximately 12 h, suggesting convection deep enough to produce robust ice scattering is a symptom of, rather than a precursor to, RI. In the eastern North Pacific basin, the amount of anomalous liquid and ice hydrometeors increases in tandem near the onset of RI. Normalized infrared and passive microwave brightness temperatures can be utilized to skillfully predict episodes of RI, as the forecast skill of RI episodes using solely normalized convective parameters is comparable to the forecast skill of RI episodes by current operational statistical models.

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Simultaneous Measurements of Atmospheric Water Vapor with MIR, Raman Lidar, and Rawinsondes

Journal of Applied Meteorology ◽

10.1175/1520-0450-34.7.1595 ◽

1995 ◽

Vol 34 (7) ◽

pp. 1595-1607 ◽

Cited By ~ 19

Author(s):

J. R. Wang ◽

S. H. Melfi ◽

P. Racette ◽

D. N. Whitemen ◽

L. A. Chang ◽

...

Keyword(s):

Water Vapor ◽

Atmospheric Water Vapor ◽

Raman Lidar ◽

Atmospheric Water ◽

Millimeter Wave Imaging ◽

Brightness Temperatures ◽

Simultaneous Measurements ◽

Wave Imaging ◽

Low Sensitivity ◽

Measured Water

Abstract Simultaneous measurements of atmospheric water vapor were made by the Millimeter-wave Imaging Radiometer (MIR), Raman lidar, and rawinsondes. Two types of rawinsonde sensor packages (AIR and Vaisala) were carried by the same balloon. The measured water vapor profiles from Raman lidar, and the Vaisala and AIR sondes were used in the radiative transfer calculations. The calculated brightness temperatures were compared with those measured from the MIR at all six frequencies (89, 150, 183.3 ± 1, 183.3 ±3, 183.3 ±7, and 220 GHz). The results show that the MIR-measured brightness temperatures agree well (within ±K) with those calculated from the Raman lidar and Vaisala measurements. The brightness temperatures calculated from the AIR sondes differ from the MIR measurements by as much as 10 K, which can be attributed to low sensitivity of the AIR sondes at relative humidity less than 20%. Both calculated and the MIR-measured brightness temperatures were also used to retrieve water vapor profiles. These retrieved profiles were compared with those measured by the Raman lidar and rawinsondes. The results of these comparisons suggest that the MIR can measure the brightness of a target to an accuracy of at most ±K and is capable of retrieving useful water vapor profiles.

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