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Global Attributes:	Conventions: CF-1.6 cdm_data_type: Grid title: Monthly average Reprocessed soil moisture maps from MetOp-ASCAT soil moisture time series for descending overpasses from the EUMETSAT H119/H120 Metop ASCAT soil moisture time series 12.5 km sampling, processing version v7 summary: The soil moisture time series data of the EUMETSAT H-SAF product H119/H120 Version 7 based on MetOp-A, -B, and -C ASCAT data, product version H119 and its extension H120, are converted into geographic maps (cartesian grid) of the 5-day average/composite soil moisture (SM) distribution separately for ascending and descending overpasses. Time series for all tiles are read, separated for ascending and descending overpasses and averaged / composited over 5 days on the original grid. Only data with known surface state flag (SSF) = 1 are used. Due to MetOp-ASCATs narrow swath overlap of adjacent swaths is quite small; hence it is rather a composite than an average and it is rarely averaged over data from 5 days. Averaging is also conducted for soil moisture noise (SMN), frozen land probability and snow cover probability. For remaining parameters values of the center day of the 5-day period are used. Processing flag (PF) and SSF are treated differently. The 5-day SSF is set to 4 (permanent ice) or 0 (unknown) if SSF=4 or SSF=0 in one of the 5 days at that location. The 5-day SSF is set to 2 (temporarily frozen) or 3 (melt water on surface or wetland fraction > 50%) if SSF=2 or SSF=3 or SSF=1 and confidence flag (new in H113) = 4 in one of the 5 days at that location. The 5-day SSF is set to 5 if a confidence flag = 16 is set for the majority of the 5-days, indicating a sensitivity to soil moisture below 1%. The 5-day SSF is set to 6 if a confidence flag = 2 or 8 is set for the majority of the 5-days, indicating topographic complexity > 50% or soil moisture noise > 50%. The 5-day PF is set to 4 (far too low SM), 8 (far too high SM), 12 (NRCS out of bounds), or 10 (wet correction applied) if the original processing flag = 1 or 2 or 4, or if the correction flag = 4 in one of the 5 days at that location. The 5-day PF is set to 1 (too low SM) or 2 (too high SM) if the correction flag = 1 or 2 in one of the 5-days at that location; if correction flags = 1 and 2 co-exist within one 5 day period the value is considered dubious and PF is set to 127. Two different 5-day SM distributions are given: one is based solely on nominally computed SM, the other one includes also those SM values which were negative (down to -25%, correction flag = 1) or positive (up to 125%, correction flag = 2) but set to 0% and 100% respectively. The same applies to SMN. SSF and PF are combined into one flag PF* as follows: PF* = SSF + PF * 10. Subsequently all data are interpolated into a cartesian grid of x- and y-dimensions of original grid. First grid cells with minimum distance to original grid positions are assigned the original data values. Second remaining gaps are filled by the average SM or SMN (or other parameters except the flag) over a 3x3 grid box centered around the grid cell with missing data. This step is repeated 4 times and a 5th time using a 5x5 grid box. For the interpolation of the flag at least three valid flag values need to exist within a 3x3 grid box and of these the one with the highest count is used to fill the missing grid cell. The threshold for the monthly average is (the number of days per Month -10). If there are fewer values per month, the value is set to the missing_value (=127). institution: Center for Earth System Research and Sustainability (CEN), University of Hamburg creator_url: https://www.cen.uni-hamburg.de/icdc creator_name: Integrated Climate Data Center (ICDC) creator_email: stefan.kern (at) uni-hamburg.de date_created: 03-Aug-2023 16:47:22 StartYear: 2007 EndYear: 2022 StartDate: 2007-01-01 EndDate: 2022-12-31 Period: 2007-2022 geographic_region: global geospatial_lat_min: -89.9437 geospatial_lat_max: 89.9437 geospatial_lon_min: -179.944 geospatial_lon_max: 179.944 geospatial_lat_resolution: 0.1126 degree geospatial_lon_resolution: 0.1123 degree time_coverage_start: 2007-01-01 time_coverage_end: 2022-12-31 time_coverage_duration: P16Y time_coverage_resolution: P1M platform: MetOp-A, MetOp-B, MetOp-C sensor: ASCAT source: H119/H120 Metop ASCAT soil moisture time series 12.5 km sampling, processing version v7, obtained from hsaf@meteoam.it in July 4 2023 references: [1] W. Wagner, G. Lemoine, and H. Rott: A method for estimating soil moisture from ERS scatterometer and soil data, Remote Sensing of Environment, 70(2), 191-207, 1999. doi: https://doi.org/10.1016/S0034-4257(99)00036-X; [2] V. Naeimi, K. Scipal, Z. Bartalis, S. Hasenauer, and W. Wagner: An Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE Trans. Geosci. Remote Sensing, 47(7), 1999-2013, 2009. doi: https://doi.org/10.1109/TGRS.2008.2011617; [3] V. Naeimi, C. Paulik, A. Bartsch, W. Wagner, R. Kidd, S.-E. Park, K. Elger, and J. Boike: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions From Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(7), 2566-2582. doi: https://doi.org/10.1109/TGRS.2011.2177667; [4] Product User Manual: H SAF, Product User Manual (PUM) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.2, 2022; [5] Algorithm Theoretical Basis Document: H SAF, Algorithm Theoretical Baseline Document (ATBD) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v0.1, 2021; [6] Product Validation Report: H SAF, Product Validation Report (PVR) Metop ASCAT Surface Soil Moisture Climate Data Record v7 12.5 km sampling (H119) and Extension (H120), v1.1, 2022.

Variables:	lat: Array of 32 bit Reals [lat = 0..1598] lat: long_name: latitude standard_name: latitude axis: Y units: degrees_north comment: uniform grid from -89.9437 to 89.9437 by 0.1126
	lon: Array of 32 bit Reals [lon = 0..3206] lon: long_name: longitude standard_name: longitude axis: X units: degrees_east comment: uniform grid from -179.944 to 179.944 by 0.1123
	time: Array of 64 bit Reals [time = 0..191] time: standard_name: time long_name: time_in_days_since_2000-01-01 units: days since 2000-01-01 comment: 15th day of month
	sm_ext: Grid time: lat: lon: _Unsigned: false long_name: Soil_Moisture_Extended units: % _FillValue: 127 missing_value: 127 comment: Computed from 5 days centered around day of this file using only data with original Surface State Flag equal to 1 and original Processing Flag equal to 0, 1, or 2
	sm_ext_noise: Grid time: lat: lon: _Unsigned: false long_name: Soil_Moisture_Extended_Noise units: % _FillValue: 127 missing_value: 127 comment: Computed from 5 days centered around day of this file using only data with original Surface State Flag equal to 1 and original Processing Flag equal to 0, 1, or 2
	n_of_sm: Grid time: lat: lon: _Unsigned: false long_name: Number_of_Soil_Moisture_Extended units: 1 _FillValue: 127 missing_value: 127
	n_of_overpasses: Grid time: lat: lon: _Unsigned: false long_name: Number_of_overpasses units: 1 _FillValue: 127 missing_value: 127 valid_min: 0 valid_max: 5 comment: Number of ascending MetOP overpasses within the 5 days considered in the respective grid cell
	snow_cover: Grid time: lat: lon: _Unsigned: false long_name: Historic_probability_of_snow_cover units: % _FillValue: 127 missing_value: 127 comment: Computed from 5 days centered around day of this file; original climatology had several grid cells wide margin around all open water areas with no snow cover values <--> inconsistent to frozen soil climatology; in this margin [1] all snow climatology grid cells are set to 0 when the yearround frozen soil climatology is 0, [2] all snow climatology grid cells of day <t> are set to 0 if the contemporary frozen land climatology indicates below 50%, [3] all snow climatology grid cells of day <t> are set to the respective contemporay frozen land climatology value if it is equal or above 50%
	frozen_soil: Grid time: lat: lon: _Unsigned: false long_name: Historic_probability_of_frozen_soil units: % _FillValue: 127 missing_value: 127 comment: Computed from 5 days centered around day of this file
	wetland: Grid time: lat: lon: _Unsigned: false long_name: Inundation_and_wetland_fraction units: % _FillValue: 127 missing_value: 127
	topography: Grid time: lat: lon: _Unsigned: false long_name: Topographic_complexity units: % _FillValue: 127 missing_value: 127
	LDAS_porosity: Grid time: lat: lon: _Unsigned: false long_name: Porosity_LDAS units: m3 m-3 _FillValue: 127 missing_value: 127 scale_factor: 0.01 comment: Porosity resampled from NASAs LDAS
	HWSD_porosity: Grid time: lat: lon: _Unsigned: false long_name: Porosity_HWSD units: m3 m-3 _FillValue: 127 missing_value: 127 scale_factor: 0.01 comment: Porosity derived from Harmonised World Soil Database (HWSD) using formulas defined by Saxton and Rawls (2006)
	status_flag: Grid time: lat: lon: _Unsigned: false long_name: Soil_Moisture status_flag units: 1 _FillValue: 127 missing_value: 127 flag_values: 0.0, 1.0, 4.0, 8.0, 2.0, 16.0, 32.0, 64.0, 127.0 flag_descriptions: 0 -> Unknown surface state, 1 -> Good retrieval, 4 -> Less good retrieval, soil moisture temporarily below 0 but above -25% or above 100 but below 125%, 8 -> Soil moisture out of bounds and/or NRCS value out of bounds, 2 -> Good retrieval but wet correction applied, 16 -> Surface temporary frozen, 32 -> Melting water on surface, 64 -> Permanent ice, 127 -> Fill comment: Composited from 5 days centered around day of this file, Comments to the wet correction from Sebastian Hahn, September 2016: The wet correction is applied in areas with rarely saturated soil moisture conditions (identified using the Koeppen Geiger climate classification classes: BWh, BWk, BSh, BSk). In such a case the wet reference is increased or corrected, because a saturated soil may never be captured by the satellite. There is neither a degradation nor an increase of the accuracy. Since the soil moisture product is a relative product a bias (=correction) in the wet reference does not really matter. The temporal dynamics stay the same, but the variance goes down. Another effect is that deserts are never at 60-100%. Without this correction this would be actually the case and would look weird on a global map with the same scaling (wet deserts). However, it is important not to forget that one grid point is not comparable to another because each has a different reference and 50% may be a lot of absolute soil moisture or almost nothing. I guess most of the people forget about the fact once they see a map.

For questions or comments about this dataset, contact the administrator of this server [Support] at: icdc.cen@lists.uni-hamburg.de

For questions or comments about the OPeNDAP service bundled with the TDS, email THREDDS support at: support-thredds@unidata.ucar.edu

DDS:

Dataset {
    Float32 lat[lat = 1599];
    Float32 lon[lon = 3207];
    Float64 time[time = 192];
    Grid {
     ARRAY:
        Byte sm_ext[time = 192][lat = 1599][lon = 3207];
     MAPS:
        Float64 time[time = 192];
        Float32 lat[lat = 1599];
        Float32 lon[lon = 3207];
    } sm_ext;
    Grid {
     ARRAY:
        Byte sm_ext_noise[time = 192][lat = 1599][lon = 3207];
     MAPS:
        Float64 time[time = 192];
        Float32 lat[lat = 1599];
        Float32 lon[lon = 3207];
    } sm_ext_noise;
    Grid {
     ARRAY:
        Byte n_of_sm[time = 192][lat = 1599][lon = 3207];
     MAPS:
        Float64 time[time = 192];
        Float32 lat[lat = 1599];
        Float32 lon[lon = 3207];
    } n_of_sm;
    Grid {
     ARRAY:
        Byte n_of_overpasses[time = 192][lat = 1599][lon = 3207];
     MAPS:
        Float64 time[time = 192];
        Float32 lat[lat = 1599];
        Float32 lon[lon = 3207];
    } n_of_overpasses;
    Grid {
     ARRAY:
        Byte snow_cover[time = 192][lat = 1599][lon = 3207];
     MAPS:
        Float64 time[time = 192];
        Float32 lat[lat = 1599];
        Float32 lon[lon = 3207];
    } snow_cover;
    Grid {
     ARRAY:
        Byte frozen_soil[time = 192][lat = 1599][lon = 3207];
     MAPS:
        Float64 time[time = 192];
        Float32 lat[lat = 1599];
        Float32 lon[lon = 3207];
    } frozen_soil;
    Grid {
     ARRAY:
        Byte wetland[time = 192][lat = 1599][lon = 3207];
     MAPS:
        Float64 time[time = 192];
        Float32 lat[lat = 1599];
        Float32 lon[lon = 3207];
    } wetland;
    Grid {
     ARRAY:
        Byte topography[time = 192][lat = 1599][lon = 3207];
     MAPS:
        Float64 time[time = 192];
        Float32 lat[lat = 1599];
        Float32 lon[lon = 3207];
    } topography;
    Grid {
     ARRAY:
        Byte LDAS_porosity[time = 192][lat = 1599][lon = 3207];
     MAPS:
        Float64 time[time = 192];
        Float32 lat[lat = 1599];
        Float32 lon[lon = 3207];
    } LDAS_porosity;
    Grid {
     ARRAY:
        Byte HWSD_porosity[time = 192][lat = 1599][lon = 3207];
     MAPS:
        Float64 time[time = 192];
        Float32 lat[lat = 1599];
        Float32 lon[lon = 3207];
    } HWSD_porosity;
    Grid {
     ARRAY:
        Byte status_flag[time = 192][lat = 1599][lon = 3207];
     MAPS:
        Float64 time[time = 192];
        Float32 lat[lat = 1599];
        Float32 lon[lon = 3207];
    } status_flag;
} ascat_soilmoisture_desc;

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