Steinar Eastwood

A Real-Time Operational Forecast Model for Meteorology and Air Quality During Peak Air Pollution Episodes in Oslo, Norway

A real-time operational forecast model for meteorology and air quality for Oslo, Norway is presented. The model systemconsists of an operational meteorological forecasts modeland an air quality model. A non-hydrostatic model operatedon two different domains with 1 and 3 km horizontalresolution is nested within the routine meteorologicalforecast model, which is run for North West Europe with 10 kmhorizontal resolution. The meteorological data are applied to an air quality model of Oslo with a 1 km grid and sub-grid treatment of line and point sources. Resultsfrom 22 days during the winter season 1999–2000 arepresented and discussed. Prediction of wind speed anddirections and relative humidity are clearly improved byincreasing the horizontal resolution of the meteorologicalmodel. Temperature inversion strengths are howeverconsiderably overestimated. The predictions of PM10corresponds best with measurements on winter days with wetor frozen surfaces in the city. On dry days, especiallyduring spring time with a large deposit of accumulated duston the roadside, the model under predicts the PM10concentrations considerably. It is in particularrecommended to improve the description of the PM10source strength in order to enhance the precision in theair quality forecasts.

Use of C-Band Scatterometer for Sea Ice Edge Identification

This paper describes use of Advanced Scatterometer (ASCAT) C-band scatterometer data for determination of the sea ice edge. The variation in backscatter with measurement geometry is different for the sea ice surface compared to the open water surface. Utilizing the ASCAT antenna configuration with three different look angles for the same surface spot, a new ASCAT sea ice parameter has been defined. One year of ASCAT measurements has been collocated with background sea ice information to derive probability distributions for the ASCAT sea ice parameter given the known ice condition. The result can be used in an inverse methodology, a Bayesian approach, to calculate the probability of sea ice from the ASCAT measurements. The method has been tested for a full year and validated against high-resolution satellite images and ice charts from an operational Ice Service. It reveals a realistic ice edge result, however, with weather-induced noise problems in terms of “false sea ice.” For automatic ice edge detection, ancillary information is needed to remove this noise. The paper shows how the method also can be used for ice edge detection with passive microwave data from SSM/I, and how it can be extended to utilize ASCAT together with SSM/I in a multisensor approach.

Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data

Cloud detection is a source of significant errors in retrieval of sea surface temperature (SST). We apply a Bayesian cloud detection scheme to 37 years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data, which is an important source of multi-decadal global SST information. The Bayesian scheme calculates a probability of clear-sky for each image pixel, conditional on the satellite observations and prior probability. We compare the cloud detection performance to the operational Clouds from AVHRR Extended algorithm (CLAVR-x), as a measure of improvement from reduced cloud-related errors. To do this we use sea surface temperature differences between satellite retrievals and in situ observations from drifting buoys and the Global Tropical Moored Buoy Array (GTMBA). The Bayesian scheme reduces the absolute difference between the mean and median SST biases and reduces the standard deviation of the SST differences by ~10% for both daytime and nighttime retrievals. These reductions are indicative of removing cloud contaminated outliers in the distribution, as these fall only on one side of the distribution forming a cold tail. At a probability threshold of 0.9 typically used to determine a binary cloud mask for SST retrieval, the Bayesian mask also reduces the robust standard deviation by ~5–10% during the day, in comparison with the operational cloud mask. This shows an improvement in the central distribution of SST differences for daytime retrievals.