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  • Author or Editor: P. Anil Rao x
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P. Anil Rao
and
Henry E. Fuelberg

Abstract

The Geostationary Operational Environmental Satellite (GOES-8) temperature–moisture retrievals were compared with collocated National Weather Service radiosonde observations (RAOBs) to assess retrieval performance. Retrieved values of temperature and dewpoint were evaluated at individual levels. Precipitable water and thickness also were evaluated, and the GOES-8 retrievals were compared with the first-guess data used in the algorithm. The dataset consisted of 1113 RAOB–retrieval pairs (collocated to within 50 km) over the United States at 1200 UTC during August–November 1995.

GOES-8 temperature retrievals were found to agree better with their RAOB-derived counterparts than did the dewpoints. However, both temperatures and dewpoints were found to be highly dependent on their first-guess data from the Nested Grid Model. Retrievals generally were closer to the RAOBs than was the first guess. However, this was never guaranteed, even for large first-guess discrepancies. In fact, some retrievals did not agree as well with the RAOBs as did the first guess.

GOES-8 and RAOB-derived precipitable water (PW) and thickness showed closer agreement than the level-specific data. Both integrated parameters were dependent on their first guess. However, GOES-8 and RAOB PW agreed more often in layers above the surface where the guess was less accurate.

Comparison with a previous evaluation of retrievals from the Visible and Infrared Spin Scan Radiometer Atmospheric Sounder (VAS) indicated that GOES-8 retrievals agreed better with RAOBs than did the VAS versions. This improvement is likely due to GOES-8’s increased number of channels and better signal-to-noise values, along with the assumed increase in quality of the first-guess data being used.

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P. Anil Rao
and
Henry E. Fuelberg

Abstract

Statistical algorithms are developed to diagnose the vertical change in equivalent potential temperature (ΔΘ e ) between 920 and 620 hPa from GOES-8 radiance data. The models are prepared using a training dataset of radiosonde releases from 10 United States cities. Simulated GOES-8 channel brightness temperatures are calculated from these soundings. The training data are stratified into several subsets (depending on time and location). Models trained only on 0000 or 1200 UTC data explain approximately 7% more of the variance in observed ΔΘ e than those trained on both 0000 and 1200 UTC. Values of R 2 from models using training data from only one are superior to those trained on multiple stations. Inclusion of the imager channels adds little information to the algorithms.

These models then are applied to data from the Limited Area Mesoscale Prediction System model to see which performs consistently better over diurnally varying conditions. Models trained only with 0000 UTC data give the best results, explaining between 63% and 81% of the variance in the independent data. The model that performed best is studied further. Biases are present when this model is applied to times other than 0000 UTC. These biases are caused by temperature differences between the 0000 UTC training data and those at the times being examined. Strong regional biases also occur when a model trained on only one location is applied to a large area. A second model is incorporated into the procedure to reduce this bias. The two-model algorithm explains more variance than the initial one-model version (93% vs 77%), and the area of strong regional bias is greatly reduced.

This statistical procedure for ΔΘ e is then tested on observed GOES-8 data. A new statistical model is formed using the observed GOES-8 brightness temperatures and ΔΘ e ’s calculated from collocated radiosonde observations. The new model yields an R 2 of 67% when applied to an independent dataset. This value is smaller than those from the models using simulated data, most likely due to several additional sources of discrepancy. Finally, simulated GOES-7 Visible Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) radiances are used to prepare a ΔΘ e algorithm. The VAS model explains approximately 5% less variance than its GOES-8 counterpart, due to the reduced vertical resolution available on VAS.

These analyses show that regionally trained regression models can accurately diagnose convective instability while using relatively little computational time.

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Henry E. Fuelberg
,
P. Anil Rao
, and
Donald W. Hillger

Abstract

Satellite-derived profiles of temperature and dewpoint (retrievals) are obtained using radiance data from the Visible-infrared Spin Scan Radiometer Atmospheric Sounder. Individual fields of view that are input to the retrieval algorithm must be horizontally averaged to provide suitable signal-to-noise ratios. This paper investigates three methods for performing this averaging: 1)a blocking approach that is employed operationally, 2) a manual procedure that seeks to maximize atmospheric gradients, and 3) an objective procedure called clustering that takes advantage of similarities in satellite measurements to avoid smearing the gradient information. The three techniques are examined on 10–11 July 1989 when intense gradients of humidity were present over the Florida peninsula.

Results show that the clustering scheme produced retrievals that were very similar to those obtained manually. Both schemes indicated strong humidity gradients in the lower troposphere. The blocking procedure produced less intense gradients. The retrieval information is used to examine conditions leading to fair weather on 10 July but intense thunderstorm development on 11 July.

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P. Anil Rao
,
Christopher S. Velden
, and
Scott A. Braun

Abstract

Errors in the height assignment of some satellite-derived winds exist because the satellites sense radiation emitted from a finite layer of the atmosphere rather than a specific level. Problems in data assimilation may arise because the motion of a measured layer is often represented by a single-level value. In this research, Geostationary Operational Environmental Satellite (GOES)–derived cloud and water-vapor motion winds are compared with collocated rawinsonde observations (raobs). The satellite winds are compared with the entire profile of the collocated raob data to determine the vertical error characteristics of the satellite winds. These results are then tested in numerical weather prediction. Comparisons with the entire profile of the collocated raobs indicate that clear-air water-vapor winds represent deeper layers than do either infrared or water-vapor cloud-tracked winds. In addition, it is found that if the vertical gradient of moisture is smooth and uniform from near the height assignment upward, the clear-air water-vapor wind tends to represent a deeper layer than if the moisture gradient contains a sharp peak. The information from the comparisons is then used in numerical model simulations of two separate events to test the results. In the first case, the use of the satellite data results in improved storm tracks during the initial ∼24-h forecast period. Mean statistics indicate that the use of satellite winds generally improves the simulation with time. The simulation results suggest that it is beneficial to spread the satellite wind information over multiple levels, particularly when the moisture profile is used to define the vertical influence.

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