Change search
Refine search result
1 - 22 of 22
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1. Alberoni, P P
    et al.
    Andersson, T
    SMHI.
    Mezzasalma, P
    Michelson, Daniel
    SMHI, Core Services.
    Nanni, S
    Use of the vertical reflectivity profile for identification of anomalous propagation2001In: Meteorological Applications, ISSN 1350-4827, E-ISSN 1469-8080, Vol. 8, no 3, p. 257-266Article in journal (Refereed)
    Abstract [en]

    Anomalous propagation (anaprop), analogous to the upper mirage in the visual wavelengths, is still a major problem in radar meteorology. This phenomenon assumes particular importance in automatic recognition and estimation of rainfall. Anaprop echoes from terrain features such as hills and coasts Often give echoes up to 50-60 dBZ equivalent to heavy rain or hail in severe thunderstorms. Anaprop echoes from sea waves may be comparable in strength to those from moderate precipitation and also form similar patterns. Based on the evidence that the vertical reflectivity profile of precipitation is quite different from the anaprop profile, two methods for anaprop identification are presented. The method proposed by the Servizio Meteorologico Regionale (SMR, Italy) simply uses the operational scan procedure to discriminate between precipitation and anaprop. At the Swedish Meteorological and Hydrological Institute an 'ad hoc' scan strategy has been developed in order to obtain much more detail of the lowest reflectivity profile. A number of statistical parameters have been used to achieve a better discrimination between precipitation, land and sea clutter. A number of case studies, representing different echo intensities and patterns, and including a case of anaprop with embedded precipitation, are presented to assess the impact of these methods.

  • 2. Bennartz, R
    et al.
    Michelson, Daniel
    SMHI, Core Services.
    Correlation of precipitation estimates from spaceborne passive microwave sensors and weather radar imagery for BALTEX PIDCAP2003In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 24, no 4, p. 723-739Article in journal (Refereed)
    Abstract [en]

    This paper describes the evaluation of a-combined radar and passive microwave dataset obtained during the PIDCAP study of the Baltic Sea Experiment (BALTEX), where three-dimensional volumes of data from the Gotland radar were obtained timed according to the overpasses of the DMSP-satellites F10 and F13. Both satellites are 'equipped with a Special Sensor Microwave/Imager (SSM/I), suitable for precipitation retrievals. We compare radar precipitation estimates, convolved to the native resolution of the SSM/I, at different altitudes with polarization and scattering indices (S-85) derived from the SSM/I. For all 22 overpasses investigated here radar precipitation estimates at 3-4 km altitude correlate well with the SSM/I-derived S-85 (average correlation coefficient = 0.70). Although more directly linked to surface precipitation, polarization indices have been found to be less correlated with radar data, due to limitations inherent in the remote sensing of precipitation at higher latitudes. A stratification of the dataset into frontal and convective events revealed significant variations in these relationships for different types of precipitation events, thus reflecting different cloud microphysical processes associated with precipitation initialization. The relationship between S85 and radar rain estimates at higher altitudes varies considerably for different convective and frontal events. The sensitivity of S-85 to radar-derived rain rate ranges from 3.1 K mm(-1) h(-1) for a strong convective event to about 25 K mm(-1) h(-1) for the frontal and about 70 mm(-1) h(-1) for the small-scale convective events. For extrapolated surface precipitation estimates, sensitivities decrease to 14 mm(-1) h(-1) and 25 mm(-1) h(-1) for frontal and small-scale convective precipitation, respectively.

  • 3. Bennartz, R
    et al.
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Dybbroe, Adam
    SMHI, Core Services.
    Michelson, Daniel
    SMHI, Core Services.
    Precipitation analysis using the Advanced Microwave Sounding Unit in support of nowcasting applications2002In: Meteorological Applications, ISSN 1350-4827, E-ISSN 1469-8080, Vol. 9, no 2, p. 177-189Article in journal (Refereed)
    Abstract [en]

    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.

  • 4.
    Bennartz, Ralf
    et al.
    SMHI.
    Thoss, Anke
    SMHI, Research Department, Atmospheric remote sensing.
    Dybbroe, Adam
    SMHI, Core Services.
    Michelson, Daniel
    SMHI, Research Department, Atmospheric remote sensing.
    Precipitation Analysis from AMSU (Nowcasting SAF)1999Report (Other academic)
  • 5. Hansson, M
    et al.
    Michelson, Daniel
    SMHI, Core Services.
    Integration of radar with analysis fields - Bringing distant radar observations "down to Earth"2000In: Physics and chemistry of the earth. Part B: Hydrology, oceans and atmosphere, ISSN 1464-1909, E-ISSN 1873-4677, Vol. 25, no 10-12, p. 1033-1036Article in journal (Refereed)
    Abstract [en]

    Normal refraction of radar waves through the atmosphere prevents radar data at distant ranges from being quantitatively representative for the Earth's surface unless precipitation-related processes taking place between the radar echo and the surface are taken into account. A one-dimensional model is presented which uses a physical description of the processes involved in obtaining surface precipitation rate from a radar echo aloft, comprising local production, coalescence and evaporation. One or two cloud layers are assumed depending on the cloud base height. Input data are operationally analysed cloud base height, temperature, and humidity along with radar observations and their altitudes. At the moment, only precipitation as liquid water is assumed in the model. The method and results using it will be presented and discussed. (C) 2000 Elsevier Science Ltd. All rights reserved.

  • 6. Heistermann, M.
    et al.
    Collis, S.
    Dixon, M. J.
    Giangrande, S.
    Helmus, J. J.
    Kelley, B.
    Koistinen, J.
    Michelson, Daniel
    SMHI, Core Services.
    Peura, M.
    Pfaff, T.
    Wolff, D. B.
    THE EMERGENCE OF OPEN-SOURCE SOFTWARE FOR THE WEATHER RADAR COMMUNITY2015In: Bulletin of The American Meteorological Society - (BAMS), ISSN 0003-0007, E-ISSN 1520-0477, Vol. 96, no 1, p. 117-+Article in journal (Refereed)
    Abstract [en]

    Weather radar analysis has become increasingly sophisticated over the past 50 years, and efforts to keep software up to date have generally lagged behind the needs of the users. We argue that progress has been impeded by the fact that software has not been developed and shared as a community. Recently, the situation has been changing. In this paper, the developers of a number of open-source software (OSS) projects highlight the potential of OSS to advance radar-related research. We argue that the community-based development of OSS holds the potential to reduce duplication of efforts and to create transparency in implemented algorithms while improving the quality and scope of the software. We also conclude that there is sufficiently mature technology to support collaboration across different software projects. This could allow for consolidation toward a set of interoperable software platforms, each designed to accommodate very specific user requirements.

  • 7. Heistermann, M.
    et al.
    Collis, S.
    Dixon, M. J.
    Helmus, J. J.
    Henja, A.
    Michelson, Daniel
    SMHI, Core Services.
    Pfaff, Thomas
    An Open Virtual Machine for Cross-Platform Weather Radar Science2015In: Bulletin of The American Meteorological Society - (BAMS), ISSN 0003-0007, E-ISSN 1520-0477, Vol. 96, no 10Article in journal (Refereed)
    Abstract [en]

    In a recent BAMS article, it is argued that community-based Open Source Software (OSS) could foster scientific progress in weather radar research, and make weather radar software more affordable, flexible, transparent, sustainable, and interoperable.Nevertheless, it can be challenging for potential developers and users to realize these benefits: tools are often cumbersome to install; different operating systems may have particular issues, or may not be supported at all; and many tools have steep learning curves.To overcome some of these barriers, we present an open, community-based virtual machine (VM). This VM can be run on any operating system, and guarantees reproducibility of results across platforms. It contains a suite of independent OSS weather radar tools (BALTRAD, Py-ART, wradlib, RSL, and Radx), and a scientific Python stack. Furthermore, it features a suite of recipes that work out of the box and provide guidance on how to use the different OSS tools alone and together. The code to build the VM from source is hosted on GitHub, which allows the VM to grow with its community.We argue that the VM presents another step toward Open (Weather Radar) Science. It can be used as a quick way to get started, for teaching, or for benchmarking and combining different tools. It can foster the idea of reproducible research in scientific publishing. Being scalable and extendable, it might even allow for real-time data processing.We expect the VM to catalyze progress toward interoperability, and to lower the barrier for new users and developers, thus extending the weather radar community and user base.

  • 8. Huuskonen, Asko
    et al.
    Haase, Günther
    SMHI, Research Department, Atmospheric remote sensing.
    Michelson, Daniel
    SMHI, Research Department, Atmospheric remote sensing.
    Leijnse, Hidde
    Holleman, Iwan
    Probert, Matt
    Gill, Rashpal
    Frech, Michael
    Kurri, Mikko
    Hohti, Harri
    Operational Solar Monitoring for Improving theHomogeneity of the European Radar Network2018In: ERAD 2018 10TH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY & HYDROLOGY / [ed] Vos, Lotte de; Leijnse, Hidde; Uijlenhoet, Remko, 2018, p. 594-600Conference paper (Other academic)
  • 9. Koistinen, J
    et al.
    Michelson, Daniel
    SMHI, Core Services.
    BALTEX weather radar-based precipitation products and their accuracies2002In: Boreal environment research, ISSN 1239-6095, E-ISSN 1797-2469, Vol. 7, no 3, p. 253-263Article in journal (Refereed)
    Abstract [en]

    This paper briefly reviews the measurement of precipitation by radar, discusses factors affecting the accuracy of such measurements, and outlines how such factors may be dealt with to improve the quality of precipitation measurements by radar for the purposes of the Baltic Sea Experiment (BALTEX). Precipitation products from the BALTEX Radar Network (BALTRAD) are then briefly presented, along with descriptions of how their qualities are improved, as are some new results on their accuracies. Intelligent compositing of data from a heterogeneous network, combined with innovative quality control, is shown to give high quality high resolution information for monitoring relative precipitation variability simultaneously over land and sea in both time and space. Gauge adjustment of radar-derived accumulated precipitation is shown to efficiently minimize the radar data's bias with increasing distance, thus yielding quantitatively useful datasets for application by the BALTEX community.

  • 10.
    Lindskog, Magnus
    et al.
    SMHI, Research Department, Meteorology.
    Jarvinen, H
    Michelson, Daniel
    SMHI, Core Services.
    Assimilation of radar radial winds in the HIRLAM 3D-Var2000In: Physics and chemistry of the earth. Part B: Hydrology, oceans and atmosphere, ISSN 1464-1909, E-ISSN 1873-4677, Vol. 25, no 10-12, p. 1243-1249Article in journal (Refereed)
    Abstract [en]

    During the last decade several attempts of assimilating radar wind data into atmospheric models have been reported by various research groups. Some of these are briefly reviewed here. A three-dimensional variational data assimilation (3D-Var) scheme for the High Resolution Limited Area Model (HIRLAM) forecasting system has been developed and prepared for assimilation of low elevation angle radar radial wind superobservations. The HIRLAM 3D-Var is based on a minimization of a cost function that consists of one term measuring the distance between the resulting analysis and a background field, which is a short-range forecast, and another term measuring the distance between the analysis and the observations. The development required for assimilating the radial wind data includes software for generating and managing the superobservations from polar volume data, a quality control algorithm and an observation operator for providing the model counterpart of the observation. The functionality of the components have been evaluated through assimilation experiments using data from Finnish and Swedish radars and further studies are underway. (C) 2000 Elsevier Science Ltd. All rights reserved.

  • 11.
    Lindskog, Magnus
    et al.
    SMHI, Research Department, Meteorology.
    Salonen, K
    Jarvinen, H
    Michelson, Daniel
    SMHI, Core Services.
    Doppler radar wind data assimilation with HIRLAM 3DVAR2004In: Monthly Weather Review, ISSN 0027-0644, E-ISSN 1520-0493, Vol. 132, no 5, p. 1081-1092Article in journal (Refereed)
    Abstract [en]

    A Doppler radar wind data assimilation system has been developed for the three-dimensional variational data assimilation (3DVAR) scheme of the High Resolution Limited Area Model (HIRLAM). Radar wind observations can be input for the multivariate HIRLAM 3DVAR either as radial wind superobservations (SOs) or as vertical profiles of horizontal wind obtained with the velocity-azimuth display (VAD) technique. The radar wind data handling system, including data processing, quality control, and observation operators for the 3DVAR, are described and evaluated. Background error standard deviation (sigma(b)) in observation space for wind and radial wind have been estimated by the so-called randomization method. The derived values of sigma(b) are used in the quality control of observations and also in the assignment of radar wind observation error standard deviations (sigma(o)). Parallel data assimilation and forecast experiments confirm reasonably tuned error statistics and indicate a small positive impact of radar wind data on the verification scores, for both inputs.

  • 12.
    Michelson, Daniel
    SMHI, Core Services.
    Systematic correction of precipitation gauge observations using analyzed meteorological variables2004In: Journal of Hydrology, ISSN 0022-1694, E-ISSN 1879-2707, Vol. 290, no 3-4, p. 161-177Article in journal (Refereed)
    Abstract [en]

    Precipitation gauge measurements suffer from several sources Of error, the most significant of which is the wind error caused by the flow distortion about the gauge orifice. An existing statistical Dynamic Correction Model (DCM) has been implemented with the intent to perform a systematic correction of precipitation measurements front gauges found in and near the Baltic Sea's drainage basin. The DCM implementation makes use of hourly gridded meteorological variables from an operational mesoscale analysis system. precipitation amounts are disaggregated into hourly components, corrected, and then summed back to yield corrected 12-hour accumulations. Sensitivity Studies for shielded H & H-90, Tretyakov, SMHI, and unshielded Hellmann gauge types demonstrate the behaviour of the DCM; the H & H-90 gauge requires the least amount of correction whereas the unshielded Hellmann gauge requires by far the most. This DCM implementation has been evaluated using two years of independent gauge data from the so-called Double Fence Intercomparison Reference (DFIR) gauge, along with independent H & H-90 observations, at Jokioinen, Finland. The results show that the H & H-90 gauge underestimates precipitation by around 8% on average and that the implementation appears to yield results which are fully consistent with previous findings and experience at this site. A second evaluation was performed with one year of measurements from Kiel, Germany, using data from a ship rain gauge (SRG) as a reference and data from two Hellmann gauges. one co-located with the SRG and the other 5.6 km distant. The results from this evaluation are more ambiguous but reveal both an overcorrection and an increased variability in the derived relation compared with uncorrected observations, one explanation being a well shielded site which the method, by its general nature, does not take into account. Although uncertainties remain in the treatment of measurements from some gauge types, systematic correction using this DCM should lead to more accurate measurements for use in hydrometeorological applications. (C) 2004 Elsevier B.V. All rights reserved.

  • 13.
    Michelson, Daniel
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Andersson, Tage
    SMHI, Research Department.
    Koistinen, Jarmo
    Collier, Christopher G.
    Riedl, J.
    Szturc., J.
    Nielsen, A.
    Overgaard Persson, T.
    BALTEX Radar Data Centre Products and their Methodologies2000Report (Other academic)
  • 14.
    Michelson, Daniel
    et al.
    SMHI, Core Services.
    Foltescu, Valentin
    SMHI.
    Häggmark, Lars
    SMHI, Core Services.
    Lindgren, Bo
    SMHI.
    MESAN Mesoscale analysis of precipitation2000In: Meteorologische Zeitschrift, ISSN 0941-2948, E-ISSN 1610-1227, Vol. 9, no 2, p. 85-96Article in journal (Refereed)
    Abstract [en]

    The Mesoscale Analysis System (MESAN) has been running operationally since April, 1997, providing science and consumers of weather information with spatially continuous fields of nine analysed meteorological parameters every hour. Data input to MESAN consists of surface observations from different observation systems, numerical weather prediction model fields, weather radar and satellite imageries, and climate information. Each data source is quality controlled before being subjected to an optimal interpolation (OI) scheme, together with data from the other sources. This paper presents MESAN's accumulated precipitation product. The methods used for interpolation of the multisource data are presented and discussed, as are the methods used to quality control each data source. Results from August-October 1995, using multisource data including gauge observations from the countries in the Baltic Sea Experiment (BALTEX) Region, exemplify the product. OI, used with a variable first guess error, has been compared with conventional inverse distance interpolation of precipitation in two catchments in mountainous terrain. Verification was conducted through modelled runoff, using areally integrated accumulated precipitation, compared with hydrograph observations. Significant improvements using OI were found in one of the catchments. The relative contribution (or importance) of each data source to the analysis has been evaluated using cross validation. Results show that gauge networks are the single most important sources and that radar imagery makes a significant contribution in areas lacking networks of dense gauges, such as the Baltic Sea. Analysis quality improves with the use of a greater number of input data sources. MESAN is an appropriate tool for creating an overall best estimate precipitation analysis and should be useful in applications where such information is required. In validating precipitation produced by numerical weather prediction models. analyses generated without the use of such model fields is recommended.

  • 15.
    Michelson, Daniel
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Henja, Anders
    SMHI.
    Ernes, Sander
    Haase, Günther
    SMHI, Research Department, Atmospheric remote sensing.
    Koistinen, Jarmo
    Ośródka, Katarzyna
    Peltonen, Tuomas
    Szewczykowski, Maciej
    Szturc, Jan
    BALTRAD Advanced Weather Radar Networking2018In: Journal of Open Research Software, ISSN 2049-9647, no 3, article id 12Article in journal (Refereed)
  • 16.
    Michelson, Daniel
    et al.
    SMHI, Core Services.
    Jones, Colin
    SMHI, Research Department, Climate research - Rossby Centre.
    Landelius, Tomas
    SMHI, Research Department, Atmospheric remote sensing.
    Collier, C G
    Haase, Gunther
    SMHI, Research Department, Atmospheric remote sensing.
    Heen, M
    'Down-to-Earth' modelling of equivalent surface precipitation using multisource data and radar2005In: Quarterly Journal of the Royal Meteorological Society, ISSN 0035-9009, E-ISSN 1477-870X, Vol. 131, no 607, p. 1093-1112Article in journal (Refereed)
    Abstract [en]

    The estimation of surface rainfall from reflectivity data derived from weather radar has been much studied over many years. It is now clear that central to this problem is the adjustment of these data for the impacts of vertical variations in the reflectivity. In this paper a new procedure (known as Down-to-Earth, DTE) is proposed and tested for combining radar measurements aloft with information from a numerical weather-prediction (NWP) model and an analysis system. The procedure involves the exploitation of moist cloud physics in an attempt to account for physical processes impacting on precipitation during its descent from the height of radar echo measurements to the surface. The application of DTE leads to increased underestimation in the radar measurements compared to precipitation gauge observations at short and intermediate radar ranges (0-120 km), but is successful at reducing the bias at further ranges. However the application of DTE does not lead to significant decreases in the random error of the surface rain rate estimate. No improvement is made when attempting to account for the precipitation phase measured by radar. It is concluded that further work on radar data quality control, along with improvements to the NWP model, are essential to improve upon results using such a physically based procedure.

  • 17.
    Michelson, Daniel
    et al.
    SMHI, Core Services.
    Koistinen, J
    Gauge-radar network adjustment for the Baltic Sea Experiment2000In: Physics and chemistry of the earth. Part B: Hydrology, oceans and atmosphere, ISSN 1464-1909, E-ISSN 1873-4677, Vol. 25, no 10-12, p. 915-920Article in journal (Refereed)
    Abstract [en]

    The gauge adjustment technique used to produce 2 x 2 km 3- and 12-hour radar-based accumulated precipitation datasets for the Baltic Sea Experiment is presented. The gauge adjustment technique is based on the gauge-to-radar ratio. A distance-dependent adjustment factor is derived and it is weighted against a spatially analyzed adjustment factor according to the local observation density and estimated spatial decorrelation distance. A preliminary adjustment strategy is applied in order to normalize data from many radars to a common level and to minimize the bias with gauges. The final adjustment field applied to radar accumulations is shown, through validation against independent gauge data, to minimize the bias between radar and gauge sums while raising the explained variance, compared to unadjusted radar sums. Areas not covered by radar are subjected to an optimal interpolation of systematically corrected gauge sums, and this field is merged with the gauge adjusted radar field in order to cover the entire Baltic Sea Experiment region. (C) 2000 Elsevier Science Ltd. All rights reserved.

  • 18.
    Michelson, Daniel
    et al.
    SMHI, Core Services.
    Landelius, Tomas
    SMHI, Research Department, Atmospheric remote sensing.
    Jones, Colin
    SMHI, Research Department, Climate research - Rossby Centre.
    Collier, C. G.
    Attempts to parameterize cloud water profiles using a neural network2004In: Atmospheric Science Letters, ISSN 1530-261X, E-ISSN 1530-261X, Vol. 5, no 7, p. 141-145Article in journal (Refereed)
    Abstract [en]

    Atmospheric state variables from a Numerical Weather Prediction (NWP) model are combined with analyzed cloud base heights in a neural network, with the objective to model corresponding cloud water profiles. It was found that the neural network was incapable of resolving the inherently non-linear vertical cloud water distributions. Copyright (C) 2004 Royal Meteorological Society

  • 19.
    Michelson, Daniel
    et al.
    SMHI, Core Services.
    Liljeberg, B M
    Pilesjo, P
    Comparison of algorithms for classifying Swedish landcover using Landsat TM and ERS-1 SAR data2000In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 71, no 1, p. 1-15Article in journal (Refereed)
    Abstract [en]

    Sixteen landcover classes in a representative Swedish environment were analyzed and classified using one Landsat TM scene and seven ERS-1 SARPRI images acquired during 1993. Spectral and backscattering signature separabilities are analyzed using the Jeffries-Matusita distance measure to determine which combinations of channels/images contained the most information. Maximum likelihood, sequential maximum a posteriori (SMAP, a Bayesian image segmentation algorithm), and back propagation neural network classification algorithms were applied and their performances evaluated. Results of the separability analyses indicated that the multitemporal SAR data contained more separable landcover information than did the multispectral TM data; the highest separabilities were achieved when the TM and SAR data were combined. Classification accuracy evaluation results indicate that the SMAP algorithm out-performed the maximum likelihood algorithm which, in turn, outperformed the neural network algorithm. The best KAPPA values, using combined data, were 0.495 for SMAP, 0.0445 for maximum likelihood, and 0.432 for neural network. Corresponding overall accuracy values were 57.1%, 52.4%, and 51.2%, respectively. A comparison between lumped crop area statistics with areal sums calculated from the classified satellite data gave the highest correspondence where the SMAP algorithm was used, followed by the maximum likelihood and neural network algorithms. Based on our application, we can therefore confirm the value of a multisource optical/SAR approach for analyzing landcover and the improvements to classification achieved using the SMAP algorithm. (C)Elsevier Science Inc., 2000.

  • 20.
    Michelson, Daniel
    et al.
    SMHI, Core Services.
    Sunhede, D
    Spurious weather radar echo identification and removal using multisource temperature information2004In: Meteorological Applications, ISSN 1350-4827, E-ISSN 1469-8080, Vol. 11, no 1, p. 1-14Article in journal (Refereed)
    Abstract [en]

    A simple and pragmatic method utilising the difference between analysed near-surface and Meteosat IR temperatures (DeltaT) is presented and applied with the aim of identifying and removing non-precipitation echoes in weather radar composite imagery. Despite inherent deficiencies in these multisource data, such as lower spatial and temporal resolutions relative to the radar data, DeltaT is demonstrated to efficiently identify efficiently those areas void of potentially precipitating clouds, and to remove radar echoes in them. A set of 243 manually analysed composites from the summer of 2000 was used to evaluate the method. False alarm rates (FAR), percent correct (PC) and Hanssen-Kuipers skill (HKS) scores were calculated from standard contingency tables for five echo classes: weak, strong, land, sea, and all. FAR was lowered in all classes, PC was generally raised by a few percent to be over 95%, while HKS either remained unchanged or was slightly lowered through the application of DeltaT. These results indicate that DeltaT successfully removes a significant amount of non-precipitation, sometimes at the expense of a small amount of true precipitation. This penalty is larger over sea, which indicates that the method may need to be tuned differently for land and sea environments. This method may act as a foundation on which improvements to radar data quality control can be made with the introduction Of new and improved satellite instrumentation such as that found on board the Meteosat Second Generation platform. However, this type of method should remain complementary to improved signal processing and radar data analysis techniques.

  • 21. Raschke, E
    et al.
    Meywerk, J
    Warrach, K
    Andrae, Ulf
    SMHI, Research Department, Meteorology.
    Bergström, Sten
    SMHI, Research Department, Hydrology.
    Beyrich, F
    Bosveld, F
    Bumke, K
    Fortelius, C
    Graham, Phil
    SMHI, Research Department, Climate research - Rossby Centre.
    Gryning, S E
    Halldin, S
    Hasse, L
    Heikinheimo, M
    Isemer, H J
    Jacob, D
    SMHI.
    Jauja, I
    Karlsson, Karl-Göran
    SMHI, Research Department, Atmospheric remote sensing.
    Keevallik, S
    Koistinen, J
    van Lammeren, A
    Lass, U
    Launianen, J
    Lehmann, A
    Liljebladh, B
    Lobmeyr, M
    Matthaus, W
    Mengelkamp, T
    Michelson, Daniel
    SMHI, Core Services.
    Napiorkowski, J
    Omstedt, Anders
    SMHI, Research Department, Oceanography.
    Piechura, J
    Rockel, B
    Rubel, F
    Ruprecht, E
    Smedman, A S
    Stigebrandt, A
    The Baltic Sea Experiment (BALTEX): A European contribution to the investigation of the energy and water cycle over a large drainage basin2001In: Bulletin of The American Meteorological Society - (BAMS), ISSN 0003-0007, E-ISSN 1520-0477, Vol. 82, no 11, p. 2389-2413Article, review/survey (Refereed)
    Abstract [en]

    The Baltic Sea Experiment (BALTEX) is one of the five continental-scale experiments of the Global Energy and Water Cycle Experiment (GEWEX). More than 50 research groups from 14 European countries are participating in this project to measure and model the energy and water cycle over the large drainage basin of the Baltic Sea in northern Europe. BALTEX aims to provide a better understanding of the processes of the climate system and to improve and to validate the water cycle in regional numerical models for weather forecasting and climate studies. A major effort is undertaken to couple interactively the atmosphere with the vegetated continental surfaces and the Baltic Sea including its sea ice. The intensive observational and modeling phase BRIDGE, which is a contribution to the Coordinated Enhanced Observing Period of GEWEX, will provide enhanced datasets for the period October 1999-February 2002 to validate numerical models and satellite products. Major achievements have been obtained in an improved understanding of related exchange processes. For the first time an interactive atmosphere-ocean-land surface model for the Baltic Sea was tested. This paper reports on major activities and some results.

  • 22.
    Rutgersson, Anna
    et al.
    SMHI, Research Department, Climate research - Rossby Centre.
    Bumke, K
    Clemens, M
    Foltescu, Valentin
    SMHI.
    Lindau, R
    Michelson, Daniel
    SMHI, Core Services.
    Omstedt, Anders
    SMHI, Research Department, Oceanography.
    Precipitation estimates over the Baltic Sea: Present state of the art2001In: Nordic Hydrology, ISSN 0029-1277, E-ISSN 1996-9694, Vol. 32, no 4-5, p. 285-314Article in journal (Refereed)
    Abstract [en]

    Precipitation is one of the main components in the water balance, and probably the component determined with the greatest uncertainties. In the present paper we focus on precipitation (mainly rain) over the Baltic Sea as a part of the BAL-TEX project to examine the present state of the art concerning different precipitation estimates over that area. Several methods are used, with the focus on 1) interpolation of available synoptic stations; 2) a mesoscale analysis system including synoptic, automatic, and climate stations, as well as weather radar and an atmospheric model; and 3) measurements performed on ships. The investigated time scales are monthly and yearly and also some long-term considerations are discussed. The comparison shows that the differences between most of the estimates, when averaged over an extended period and a larger area, are in the order of 10-20%, which is in the same range as the correction of the synoptic gauge measurements due to wind and evaporation losses. In all data sets using gauge data it is important to include corrections for high winds. To improve the structure of precipitation over sea more focus is to be put on the use of radar data and combinations of radar data and other data. Interpolation methods that do not consider orographic effects must treat areas with large horizontal precipitation gradients with care. Due to the large variability in precipitation in time and space, it is important to use long time periods for climate estimates of precipitation. Ship measurements are a valuable contribution to precipitation information over sea, especially for seasonal and annual time scales.

1 - 22 of 22
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
v. 2.35.3
|