Diagnosing Convective Instability fromGOES-8Radiances

Abstract

Statistical algorithms are developed to diagnose the vertical change in equivalent potential temperature (⌬⌰ 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 ⌬⌰ 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 ⌬⌰ e is then tested on observed GOES-8 data. A new statistical model is formed using the observed GOES-8 brightness temperatures and ⌬⌰ 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 ⌬⌰ 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.

The combined instability index: a new very‐short range convection forecasting technique for southern Africa

“…One way of achieving this is through the Global Instability Indices (GII), where Meteosat Second Generation (MSG) fields and model fields are combined into one product to provide instability indices independent of upper air sounding sites. These types of retrievals of instability and air mass parameters have been used operationally since 1988 using first the GOES VISSR Atmospheric Sounder (VAS) instrument and later the GOES Sounder (Hayden, 1988; Huang et al , 1992; Rao and Fuelberg, 1997; Menzel et al , 1998; Dostalek and Schmit, 2001; Schmit et al , 2002). The greatest advantage of these fields is the added capability of the nearly continuous monitoring of the instability fields guaranteed by the MSG 15 min repeat cycle.…”

An Evaluation ofGOES-8Retrievals

“…Ellrod (1998) is using GOES data to produce a forecasting product for microbursts. Furthermore, Rao and Fuelberg (1997) used GOES-8 radiances to determine areas of convective instability. Although Schmit (1996) compared some GOES-8 moisture retrievals with RAOBs, a comprehensive evaluation of GOES-8 temperature and moisture retrievals has not yet been reported in the formal literature.…”

The MSG Global Instability Indices Product and Its Use as a Nowcasting Tool

“…Various indices are used by forecasters for different applications and regions, and these indices are defined as the differences in profile parameters at different pressure levels (Galway 1956;Kurz 1993). Such data are usually derived from radio soundings, but a few satellite-derived indices also exist [e.g., derived from Geostationary Operational Environmental Satellites (GOES); Hayden (1988); Rao and Fuelberg (1997)]. The airmass parameters can be used to issue severe weather warnings if the corresponding index exceeds a certain threshold.…”