A cloud detection algorithm for high‐spectral‐resolution infrared sounders

121

100

The status of current efforts to assimilate cloud-and precipitation-affected satellite data is summarised with special focus on infrared and microwave radiance data obtained from operational Earth observation satellites. All global centres pursue efforts to enhance infrared radiance data usage due to the limited availability of temperature observations in cloudy regions where forecast skill is estimated to strongly depend on the initial conditions. Most systems focus on the sharpening of weighting functions at cloud top providing high vertical resolution temperature increments to the analysis, mainly in areas of persistent high and low cloud cover. Microwave radiance assimilation produces impact on the deeper atmospheric moisture structures as well as cloud microphysics and, through control variable and background-error formulation, also on temperature but to lesser extent than infrared data. Examples of how the impacts of these two observation types are combined are shown for subtropical low-level cloud regimes. The overall impact of assimilating such data on forecast skill is measurably positive despite the fact that the employed assimilation systems have been constructed and optimized for clear-sky data. This leads to the conclusion that a better understanding and modelling of model processes in cloud-affected areas and data assimilation system enhancements through inclusion of moist processes and their error characterization will contribute substantially to future forecast improvement.

Section: Infrared Radiancesmentioning

confidence: 99%

Satellite cloud and precipitation assimilation at operational NWP centres

Bauer

Auligné

Bell

et al. 2011

121

100

“…Furthermore, for cloudy pixels, active channels having a peak above the cloud top were assimilated. The cloud detection scheme used in this study is a version of McNally and Watts (2003) adapted to the IASI radiances as described in Collard and McNally (2009).…”

Section: The Iasi Data (Pre-)processingmentioning

confidence: 99%

Exploring the assimilation of IASI radiances in forecasting polar lows

Randriamampianina

Iversen

Storto

2011

We studied the use of IASI data to improve the forecasts of extreme weather events in the Arctic region. For this purpose, the HARMONIE/Norway regional model was used. A set of 366 IASI channels was initially chosen from the ECMWF archived database. Active channels showing the best fit with the analysis system were selected by applying a multi-step monitoring technique. The IASI data were assimilated together with most of the available conventional and operational satellite observations using a three-dimensional variational data assimilation system. Four experiments with cyclic assimilations and subsequent 48-hour forecasts were performed during the IPY-THORPEX campaign period to evaluate the impact of the IASI data and the campaign observations on the hydrostatic HARMONIE/Norway analyses and forecasts.The assessment of the degrees of freedom for signals on the analysis showed that incorporating the IASI data in the assimilation system improved the contribution of the other observations. The utilization of an energy norm-based approach proved the sensitivity of the forecasts to the IASI channels in cases dominated by dynamic instabilities leading to quickly developing weather systems like, for example, polar lows.Comparison of the HARMONIE/Norway forecasts against independent observations and the ECMWF analyses showed a clear positive impact of the IASI data on geopotential fields in mid-troposphere and in the troposphere in general, respectively. We found small but significant positive impact on the temperature and humidity in the lower troposphere.A case-study showed positive impact of IASI radiances on the analysis and forecasts of a polar low. The impact on the forecasts lasted up to 24 hours when extra in situ campaign data were excluded from the analysis, and up to 36 hours when the campaign data were assimilated together with the IASI radiances.

“…depending of the sensitivity to clouds in the infrared (McNally and Watts, 2003), and to water clouds and rain (Bauer et al, 2002) in the microwave measurements. The geostationary radiances and some of the higherpeaking channels of AIRS, HIRS and AMSU-B are used also over land.…”

Section: Definition Of Experimentsmentioning

confidence: 99%

Analysis and forecast impact of the main humidity observing systems

Andersson

Hólm

Bauer

et al. 2007

Self Cite

ABSTRACT:The global analysis and forecast impact of observed humidity has been assessed by means of observing system experiments with the ECMWF 4D-Var data assimilation system. It is found that humidity data have a significant impact extending into the medium range (5-6 day forecasts), with a marked impact also on the wind and temperature fields. This contradicts some previous studies that have shown insignificant impact of humidity observations in general. The current, greater benefit of the humidity analysis may be due to improved model and data assimilation methods, and vastly increased availability of atmospheric moisture observations. The results show that each tested data type provides benefit to the analysis and forecast performance, which indicates that the humidity analysis is effective in extracting information from a wide variety of humidity observations. Data from the microwave sounding instruments (SSMI and AMSU-B) dominate the humidity analysis over the sea, whereas radiosondes, surface stations (SYNOP) and AMSU-B dominate over land. The infrared sounders (GOES, HIRS and AIRS) dominate in the upper troposphere, at 200-300 hPa.The lack of absolutely calibrated humidity data makes dealing with biases in observations and model one of the main issues for determining the global moisture distribution and a balanced hydrological cycle. In these experiments, SSMI adds water in the subtropical subsidence areas due to a bias with respect to the model. In several locations over land, radiosondes and SYNOP have opposite bias impacts in the boundary layer, resulting in local influence on precipitation when either dataset is withheld. The SYNOP data are biased wet and the radiosondes are biased dry with respect to the model.