MetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

The performance of the three cloud products cloud fractional cover, cloud type and cloud top height, derived from NOAA AVHRR data and produced by the EUMETSAT Climate Monitoring Satellite Application Facility, has been evaluated in detail over the Arctic region for four months in 2007 using CALIPSO-CALIOP observations. The evaluation was based on 142 selected NOAA/Metop overpasses allowing almost 400 000 individual matchups between AVHRR pixels and CALIOP measurements distributed approximately equally over the studied months (June, July, August and December 2007). Results suggest that estimations of cloud amounts are very accurate during the polar summer season while a substantial loss of detected clouds occurs in the polar winter. Evaluation results for cloud type and cloud top products point at specific problems related to the existence of near isothermal conditions in the lower troposphere in the polar summer and the use of reference vertical temperature profiles from Numerical Weather Prediction model analyses. The latter are currently not detailed enough in describing true conditions relevant on the pixel scale. This concerns especially the description of near-surface temperature inversions which are often too weak leading to large errors in interpreted cloud top heights.

New methods and software for cloud detection and classification at high and midlatitudes using Advanced Very High Resolution Radiometer (AVHRR) data are developed for use in a wide range of meteorological, climatological, land surface, and oceanic applications within the Satellite Application Facilities (SAFs) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), including the SAF for Nowcasting and Very Short Range Forecasting Applications (NWCSAF) project. The cloud mask employs smoothly varying (dynamic) thresholds that separate fully cloudy or cloud-contaminated fields of view from cloud-free conditions. Thresholds are adapted to the actual state of the atmosphere and surface and the sun-satellite viewing geometry using cloud-free radiative transfer model simulations. Both the cloud masking and the cloud-type classification are done using sequences of grouped threshold tests that employ both spectral and textural features. The cloud-type classification divides the cloudy pixels into 10 different categories: 5 opaque cloud types, 4 semitransparent clouds, and 1 subpixel cloud category. The threshold method is fuzzy in the sense that the distances in feature space to the thresholds are stored and are used to determine whether to stop or to continue testing. They are also used as a quality indicator of the final output. The atmospheric state should preferably be taken from a short-range NWP model, but the algorithms can also run with climatological fields as input.

Algorithms for cloud detection (cloud mask) and classification (cloud type) at high and midlatitudes using data from the Advanced Very High Resolution Radiometer (AVHRR) on board the current NOAA satellites and future polar Meteorological and Operational Weather Satellites (METOP) of the European Organisation for the Exploitation of Meteorological Satellites have been extensively validated over northern Europe and the adjacent seas. The algorithms have been described in detail in Part I and are based on a multispectral grouped threshold approach, making use of cloud-free radiative transfer model simulations. The thresholds applied in the algorithms have been validated and tuned using a database interactively built up over more than 1 yr of data from NOAA-12, -14, and -15 by experienced nephanalysts. The database contains almost 4000 rectangular (in the image data)-sized targets (typically with sides around 10 pixels), with satellite data collocated in time and space with atmospheric data from a short-range NWP forecast model, land cover characterization, elevation data, and a label identifying the given cloud or surface type as interpreted by the nephanalyst. For independent and objective validation, a large dataset of nearly 3 yr of collocated surface synoptic observation (Synop) reports, AVHRR data, and NWP model output over northern and central Europe have been collected. Furthermore, weather radar data were used to check the consistency of the cloud type. The cloud mask performs best over daytime sea and worst at twilight and night over land. As compared with Synop, the cloud cover is overestimated during night (except for completely overcast situations) and is underestimated at twilight. The algorithms have been compared with the more empirically based Swedish Meteorological and Hydrological Institute (SMHI) Cloud Analysis Model Using Digital AVHRR Data (SCANDIA), operationally run at SMHI since 1989, and results show that performance has improved significantly.

We describe a method to remotely sense precipitation and classify its intensity over water, coasts and land surfaces. This method is intended to be used in an operational nowcasting environment. It is based on data obtained from the Advanced Microwave Sounding Unit (AMSU) onboard NOAA-15. Each observation is assigned a probability of belonging to four classes: precipitation-free, risk of precipitation, precipitation between 0.5 and 5 mm/h, and precipitation higher than 5 mm/h. Since the method is designed to work over different surface types, it relies mainly on the scattering signal of precipitation-sized ice particles received at high frequencies. For the calibration and validation of the method we use an eight-month dataset of combined weather radar and AMSU data obtained over the Baltic area. We compare results for the AMSU-B channels at 89 GHz and 150 GHz and find that the high frequency channel at 150 GHz allows for a much better discrimination of different types of precipitation than the 89 GHz channel. While precipitation-free areas, as well as heavily precipitating areas (> 5 mm/h), can be identified to high accuracy, the intermediate classes are more ambiguous. This stems from the ambiguity of the passive microwave observations as well as from the non-perfect matching of the different data sources and sub-optimal radar adjustment. In addition to a statistical assessment of the method's accuracy, we present case studies to demonstrate its capabilities to classify different types of precipitation and to work over highly structured, inhomogeneous surfaces.

Cloud top temperature and height estimates obtained from AVHRR infrared imagery require a correction for semi-transparency when cirrus layers are present. In this work we investigated the possibility of using the 11 μm and12 μm window channel brightness temperatures for the correction. We developed software which implements a method based on the work of Inoue (1985) and Derrien et al. (1988). In this method the cloud top temperature is derived for each small image segment by fitting a curve to . a twodimensional histogram of the segment, formed by using the brightness temperatureT ( 11 μm) and the brightness temperature diff erence T ( 11 μm) - T(l2μm). By extrapolating the model fit of the distribution to the opaque limit, a temperature estimate can be assigned to the semi-transparent cloud pixels, thereby replacing the measured brightness temperature which observes the combined background radiation and cloud emission. In this work, in addition to implementing data processing with the histogram based correction, we also developed a graphical user interface for testing the method, in order to provide a tool for the overall evaluation of the product.

The main objective of this study was to find the situations when the present implementation of the Cloud Mask and Cloud Type models are not able to correctly classify the cloud scenes or the surface features and to describe under which circumstances this occurs.

The method chosen was to campare the mode! output, i.e. the cloud type classification with the subjective, human interpretation of the satellite images resulting in cloud type. Until now, such a method has not been used in the validation of the CT and CMa products.

More than 600 pairs of Cloud Type Classification images and AVHRR 5 bands images and RGB combinations of them were analyzed in order to get information on the behavior of the Cloud Type Mode! in summer and winter conditions. In about 145 cases, the human interpreted cloud type was found to show significant differences requiring a more thorough analysis. It has been found that 8 classes of clouds in summer conditions (August 2000) and 11 classes in winter conditions (February-March 2001) were classified as other cloud types when compared to the outcome of the subjective analysis.