Nowcasting SAF. Validation of AVHRR cloud products : Visiting scientist report

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

S-NPP VIIRS SDR data 2015-03-01 02:48

<p>Suomi NPP VIIRS SDR data from the Norrköping DR station, covering Northern Europe including Scandinavia and surrounding seas.</p> <p>2015-03-01 02:48 - 03:03</p

Evaluation of Arctic cloud products from the EUMETSAT Climate Monitoring Satellite Application Facility based on CALIPSO-CALIOP observations

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

Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

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

pytroll/pygac: Pygac used for processing the Clara A2 dataset

A python package to read and calibrate NOAA AVHRR GAC dat

Nowcasting SAF - Retrieving Cloud Top Temperature and Height in Semi-transparent and Fractional Cloudiness using AVHRR

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

Investigations of NOAA AVHRR/3 1.6 m m imagery for snow, cloud and sunglint discrimination (Nowcasting SAF) : Visiting scientist report: FinnishMeteorologicallnstitute and Swedish Meteorological and Hydrological Institute

The AVHRR/3 imager on the NOAA-15 satellite (launched in May 1998) includes a new spectral channel at 1.6 μm ( denoted 3A) which has not been used earlier for operational monitoring of meteorological conditions. This channel has been available in research mode from the ATSR instrument onboard the ERS satellites and from the Thematic Mapper instrument of the Landsat satellites. However, the use of channel 3A for NOAA-15 will be restricted to a few test periods since the 3.7 μm channel (denoted 3B) will still be maintained as the operational A VHRR channel 3. Such a test period occured during the spring of 1999. From March 9th until April 20th NOAA-15 were transmitting AVHRR Channel 3A and 3B data according to the following schedule: Channel 3A was operational during daylight passes only, as the spacecraft crosses the terminator into daylight, with the additional constraint that the spacecraft subpoint is North of 40 degrees N. As the spacecraft crosses the terminator into darkness, Channel 3A is toggled off, and Channel 3B toggled on.</p

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

SCANDIA -its accuracy in classifying LOW CLOUD : An exchange-work between The Swedish Airforce and SMHI

Low clouds are of great interest for the airborne users of weather forecasts. Therefore it is important to improve the techniques of forecasting low clouds. One valuable way to detect low clouds is through the information from satellite images. A cloud classfication model (named SCANDIA - described by Karlsson, 1996) is used since many years at SMHI. Cloud classification results are distributed to users at the central forecasting office, at local forecasting offices and at forecasting offices of the Swedish Airforce. Since there are still improvements to make in cloud classification applications, the Swedish Airforce startad this project to join the development and research going on in this area at SMHI. The study focuses on low clouds. As we know from long term experience and earlier studies, the SCANDIA cloud classification model has problems in specific conditions. These situations are: Low level inversion with no significant cloud signature (due to dawn/dusk illumination or mixed water &amp; ice phases). Sunglint in combination with cold sea. Forward scattering, particularly in moist and hazy atmospheres. This document reports on the general performance of the SCANDIA cloud classification scheme concerning the treatment of low clouds. Validations and verifications have been made to identify and focus on the specific problems. A database (MSMS = Matching Satellite Model &amp; SYNOP data) was constructed and is continuously being updated and expanded. MSMS is used for the validations and verifications. By studying the information in the database from surface observations, NOAA AVHRR satellite data, and the SCANDIA classification, the problems can be identified, and same ways to improve the classification model might be found and suggested. In a wider scope, it can be seen as a preliminary study for the purpose of improving the analysis of low cloudines inferred from satellite data in the SMHI mesoscale analysis medel MESAN (Häggmark,1997)

Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (desert) aerosols in visible satellite imagery

Look Up Tables for removing atmospherical signal due to Rayleigh scattering and (desert) aerosols in visible satellite imagery LibRadTran simulations for various standard atmospheres for the correction of Rayleigh scattering and desert aerosol composition (Hess et al., 1998) within satellite images of channels in the visible spectral range