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COSMO-MED(eng)

2022-08-04T14:37:38Z

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{| class="wikitable"
|-
|style="background-color: orange;" |Layer name
|style="background-color: orange;"|COSMO MED
|-
|Tag
|Meteorological Models
|-
|Folder
|Meteorological Models
|-
|Source
|[https://www.arpae.it/it/temi-ambientali/meteo/scopri-di-piu/il-modello-meteorologico-cosmo-lami COSMO MED]
|-
|Description
|The model provides deterministic products with a limited area, useful for medium-short term forecasting (up to the expiry of +72 hours) and its definition on smaller spatial scales, is managed by [http: //www.arpae. it / sim / ARPA-SIMC Emilia Romagna] jointly with [http://www.meteoam.it/page/centro-nazionale-di-meteorologia-e-climatologia-aeronautica-cnmca CNMCA] (National Center for Aeronautical Meteorology and Climatology ) of the Air Force as part of the international collaboration project [http://www.cosmo-model.org/ COSMO]. This model runs twice a day, at 00:00 and at 12.00, providing a forecast of the two-dimensional weather fields with a horizontal resolution of about 5km, covering the whole Mediterranean territory. It is possible to select cumulative over a greater number of hours and view the results on different spatial aggregations on a regional scale, the borders of the provinces, municipal borders, Countries, weather surveillance areas, alert areas and basins.

|-
|Screenshot
|[[File:COSMOMed.png|1000px|thumb|centre|]]
|[[File:legCOSMOMed.png|800px|thumb|centre|Temperature legend]]
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|style="background-color: grey;"| Properties
|style="background-color: grey;"|
|-
|Available variables
|1 hr precipitation [mm] Snow [mm] Temperature at 2m [° C]
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|Available accumulations
|3, 6, 12, 24, 48, 72 hours (for total precipitation)
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|Available interpolation algorithms
|
|-
|Available filters
|
|-
|Spatial aggregations
|Municipalities, Provinces, Regions, World regions, Meteoclimatic Surveillance Areas, Warning Areas, Main River Basins
|}

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COSMO-5m(eng)

2022-08-04T14:19:02Z

Admin: Created page with " [Home] - [Forecast Models] {| class="wikitable" |- |style="background-color: orange;" |Layer name |style="background-color: orange;"|C..."

[[User Guide | [Home]]] - [[Forecast Models | [Forecast Models]]]

{| class="wikitable"
|-
|style="background-color: orange;" |Layer name
|style="background-color: orange;"|COSMO 5m
|-
|Tag
|Meteorological Models
|-
|Folder
|Meteorological Models
|-
|Source
|[https://www.arpae.it/it/temi-ambientali/meteo/scopri-di-piu/il-modello-meteorologico-cosmo-lami COSMO LAMI]
|-
|Description
|The model provides deterministic products with a limited area, useful for medium-short term forecasting (up to the expiry of +72 hours) and its definition on smaller spatial scales, is managed by [http: //www.arpae. it / sim / ARPA-SIMC Emilia Romagna] jointly with [http://www.meteoam.it/page/centro-nazionale-di-meteorologia-e-climatologia-aeronautica-cnmca CNMCA] (National Center for Aeronautical Meteorology and Climatology ) of the Air Force as part of the international collaboration project [http://www.cosmo-model.org/ COSMO]. This model runs twice a day, at 00.00 and 12.00, providing a forecast of the two-dimensional weather fields with a horizontal resolution of about 5km, covering the whole national territory. It is possible to select cumulative over a greater number of hours and view the results on different spatial aggregations on a regional scale, the borders of the provinces, municipal borders, Italy, weather surveillance areas, alert areas and basins.

|-
|Screenshot
|[[File:COSMO5.jpg|1000px|thumb|centre|]]
|[[File:legenda_COSMO5.png|800px|thumb|centre|Legend]]
|-
|style="background-color: grey;"| Properties
|style="background-color: grey;"|
|-
|Available variables
|Total precipitation [mm] Snow [mm] Temperature at 2m [° C] Temperature at 500hPa [° C] Temperature at 850hPa [° C] Temperature dew point at 2m [° C] Atmospheric pressure at sea level [mbar] Specific humidity at 1000hPa [kg / kg] Specific humidity at 850hPa [kg / kg] Relative humidity at 2m [%] Wind speed at 10m [m / s] Wind direction at 10m [degrees] Geopotential height at 850hPa [m] Geopotential height at 1000hPa [m]
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|Available accumulations
|3, 6, 12, 24, 48, 72 hours (for total precipitation and snow)
|-
|Available interpolation algorithms
|
|-
|Available filters
|
|-
|Spatial aggregations
|Municipalities, Provinces, Regions, Meteoclimatic Surveillance Areas, Warning Areas, Main River Basins (ISPRA)
|}

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Copernicus DMP(eng)

2022-08-04T11:57:38Z

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{| class="wikitable"
|-
|style="background-color: orange;" |Layer name
|style="background-color: orange;"| Dry Matter Productivity DMP (Copernicus)
|-
|Tag
|Land Cover
|-
|Folder
|Eo Satellite
|-
|Source
|[https://land.copernicus.eu/global/products/dmp DMP (Copernicus)]
|-
|Description
|Dry matter Productivity (DMP) represents the overall growth rate or dry biomass increase of the vegetation and is directly related to ecosystem Net Primary Productivity (NPP), however with units customized for agro-statistical purposes (kg/ha/day).

Similarly the Gross Dry Matter Productivity (GDMP) is equivalent to Gross Primary Productivity (GPP).

The main difference between DMP and GDMP lies in the inclusion of the autotrophic respiration.
More info at: [https://land.copernicus.eu/global/products/dmp Copernicus DMP documentation on line] [https://land.copernicus.vgt.vito.be/geonetwork/srv/eng/catalog.search#/metadata/urn:cgls:global:dmp_v2_1km catalog]
|-
|Screenshot
|[[File:DMP.png|1000px|thumb|centre|]]
|[[File:legend_DMP.png|800px|thumb|centre|Legend]]
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|style="background-color: grey;"|Properties
|style="background-color: grey;"|
|-

|Available variables
|Dry matter productivity
|-
|Available accumulations
|
|-
|Available interpolation algorithms
|
|-
|Available filters
|
|-
|Spatial aggregations
|-
|}

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Copernicus WB(eng)

2022-08-04T11:50:46Z

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{| class="wikitable"
|-
|style="background-color: orange;" |Layer name
|style="background-color: orange;"| Water Bodies (LSWT) (Copernicus)
|-
|Tag
|Land Cover
|-
|Folder
|Eo Satellite
|-
|Source
|[https://land.copernicus.eu/global/products/lswt LSWT (Copernicus)]
|-
|Description
|The Water Bodies product detects the areas covered by inland water along the year providing the maximum and the minimum extent of the water surface as well as the seasonal dynamics.
The area of water bodies is identified as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS).
More info at: [https://land.copernicus.eu/global/products/wb Copernicus WB documentation on line] and [https://land.copernicus.vgt.vito.be/geonetwork/srv/eng/catalog.search#/metadata/urn:cgls:global:wb_v2_1km catalog]
|-
|Screenshot
|[[File:WB.png|1000px|thumb|centre|]]
|[[File:Legend_WB.png|800px|thumb|centre|Legend]]
|-
|style="background-color: grey;"|Properties
|style="background-color: grey;"|
|-

|Available variables
| Water Bodies
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|Available accumulations
|
|-
|Available interpolation algorithms
|
|-
|Available filters
|
|-
|Spatial aggregations
|-
|}

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Copernicus LSWT(eng)

2022-08-04T11:14:35Z

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{| class="wikitable"
|-
|style="background-color: orange;" |Layer name
|style="background-color: orange;"| Lake surface water temperature (LSWT) (Copernicus)
|-
|Tag
|Land Cover
|-
|Folder
|Eo Satellite
|-
|Source
|[https://land.copernicus.eu/global/products/lswt LSWT (Copernicus)]
|-
|Description
|Lake surface water temperature (LSWT) describes the temperature of the lake surface, one important indicator of lake hydrology and biogeochemistry. Temperature trends observed over many years can be an indicator of how climate change affects the lake.
LSWT is recognized internationally as an Essential Climate Variable (ECV) and complements the [[Copernicus LWQ(eng) | Lake Water Quality]] information, in environmental monitoring of a large number of lakes globally.
More info at: [https://land.copernicus.eu/global/products/lswt Copernicus LSWT documentation on line] and [https://land.copernicus.vgt.vito.be/geonetwork/srv/eng/catalog.search#/metadata/urn:cgls:global:lswt_v1_1km_offline Catalog]
|-
|Screenshot
|[[File:LSWT.png|1000px|thumb|centre|]]
|[[File:legend_LSWT.png|800px|thumb|centre|Legend]]
|-
|style="background-color: grey;"|Properties
|style="background-color: grey;"|
|-

|Available variables
| lake surface water temperature
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|Available accumulations
|
|-
|Available interpolation algorithms
|
|-
|Available filters
|
|-
|Spatial aggregations
|-
|}

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GRACE(eng)

2022-08-04T10:27:11Z

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{| class="wikitable"
|-
|style="background-color: orange;" |Layer name
|style="background-color: orange;"| Total Water Storage Anomaly Map (GDO)
|-
|Tag
|Flood
|-
|Folder
|Eo Satellite
|-
|Source
|[https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_grace_tws_anomaly.pdf GRACE]
|-
|Description
|The Total Water Storage (TWS) Anomaly indicator that is implemented in the Copernicus Global
Drought Observatory (GDO) is used for determining the occurrence of long-term hydrological
drought conditions, which arise when the TWS reaches values lower than usual. This quantity is
often used as a proxy of groundwater drought. The TWS Anomaly indicator in GDO is computed as
anomalies of GRACE-derived TWS data - which are produced by the Center for Space Research (CSR)
at the University of Texas at Austin, as scaled by the NASA Jet Propulsion Laboratory (JPL) (available
at: [https://podaac-tools.jpl.nasa.gov/drive/files/allData/tellus/L3/gracefo/land_mass/RL06/ JPL Nasa]).
Details on the comparison between this dataset and multiple timescale SPI can be find in
Cammalleri et al. (2019)
The Total Water Storage (TWS) is the sum of all above and below surface water storages, including
canopy water, rivers and lakes, soil moisture and groundwater, and it represents a synthetic proxy
of the dynamic of slow-responding hydrological quantities. The TWS remote estimates from the
GRACE-FO pair of satellites are used to compute the anomaly from the climatological reference
period (2002-2018), and it is updated monthly, with a latency of about 45 days. The TWS Anomaly
indicator is used to detect and monitor long-term hydrological drought, which complements more
fast-responding indicators, such as soil moisture and streamflow droughts.
For further information see the [https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_grace_tws_anomaly.pdf GRACE Factsheet]
|-
|Screenshot
|[[File: GRACE.png |1000px|thumb|centre|]]
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|style="background-color: grey;"| Properties
|style="background-color: grey;"|
|-
|Available variables
| Water Storage Anomaly
|-
|Available accumulations
|
|-
|Available interpolation algorithms
|
|-
|Available filters
|
|-
|Spatial aggregations
|-
|}

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Uncertainty Values (eng)

2022-08-04T10:14:21Z

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Reference Water Mask (eng)

2022-08-04T09:59:04Z

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{| class="wikitable"
|-
|style="background-color: orange;" |Layer name
|style="background-color: orange;"| Reference Water Mask (GFM)
|-
|Tag
|Flood
|-
|Folder
|Eo Satellite
|-
|Source
|[https://extwiki.eodc.eu/GFM/PUM/Products/S-1-Reference-Water-Mask Reference Water Mask]
|-
|Description
| The '''Sentinel-1 Reference Water Mask''' identifies the pixels classified as open and calm water, both permanent and seasonal, using Sentinel-1 SAR backscatter intensity using an ensemble flood mapping algorithm. 
Whereas the permanent water extent mapping uses as input the median backscatter of all Sentinel-1 data from a time period of two years, the seasonal reference water mapping uses as input the median backscatter of all Sentinel-1 data from a given month over a two-year period. As a result, twelve masks are available, one per month, which includes information on the permanent and seasonal reference water extent. This parameter database is updated once a year. 
For example, the NRT system running in 2021 will rely on Sentinel-1 reference Water Mask extracted from the Sentinel-1 pre-processed data cube from 2019 and 2020. The algorithm relies also on offline-generated Sentinel-1 SAR parameters and auxiliary thematic datasets such as Exclusion Mask, DEM and HAND index. The Sentinel-1 Reference Water Mask values are described below. 
Further Details 
In order to identify the flood extent, the Sentinel-1 observed water extent derived by the proposed ensemble approach has to be masked by a reference water layer, which represents the extent of open water bodies under normal conditions. In the literature reference masks of the permanent water extent are often used for this purpose (Wieland and Martinis, 2019[1]). 
A truly permanent water area would mean that there was observed water coverage in every single observation of the considered time period; i.e. the ''Water Occurrence'' (WO), which is the ratio between the number of water detections during a certain time period and the number of valid observations of the same period, would be 100 %. 

In order to consider uncertainties in the single water segmentations and the occurrence of hydrological extreme events the WO threshold is usually relaxed to a value of 85-90 % (e.g. Pekel et al., 2016[2]). 

The application of a permanent water mask is reliable in environments with stable hydrological conditions over the year. In geographic regions with strong hydrological dynamics related to temporal change and variation in space of the water extent (e.g. in areas affected by monsoonal effects) the use of a permanent water mask is not reliable and has to be replaced by a seasonal reference water mask, which considers the usual water extent of the respective time period. Within this activity permanent as well as seasonal reference water masks will be computed based on historical Sentinel-1 time-series data in order to distinguish flooded areas from permanent water-bodies and seasonal water fluctuations. 

The permanent water mask will be based on applying the proposed ensemble water mapping approach on the median backscatter of all Sentinel-1 data covering a time period of two years. If, for example, the currently analysed Sentinel-1 scene is acquired in the year 2021 it will be compared by the permanent reference water mask computed from data of the two previous years 2019 and 2020. 
The seasonal reference masks will be provided on a monthly basis by applying the ensemble approach on the median backscatter derived from monthly data stacks over a time period of also two years. If, for example, the currently analysed Sentinel-1 scene is acquired in the January 2021 it will be compared by the seasonal reference water mask computed from data acquired in January of the previous years 2019 and 2020. 

The application of the ensemble approach on the median backscatter instead of computing the ''Water Occurrence'' based on each single observation has the advantage that the processing efficiency can be strongly increased. The reference water masks will be updated once a year in an offline environment in order to compare the Sentinel-1 observed water extent with the reference water extent of the two previous years. This helps to provide up-to-date reference water masks. The reference period of two years is chosen as compromise in order to base the computation on a sufficient number of Sentinel-1 data and to remove long-term hydrologic changes which would be integrated in the reference waters masks if longer time periods would be considered.

====References====
[1] [https://doi.org/10.3390/rs11192330 Wieland M. and Martinis S. A Modular Processing Chain for Automated Flood Monitoring from Multi-Spectral Satellite Data, Remote Sensing, 11(19), 2330, 2019.Wilks, D. S. (2011). Statistical methods in the atmospheric sciences (Vol. 100). Academic press]

[2] [https://www.nature.com/articles/nature20584 Pekel, J. F., Cottam, A., Gorelick, N., & Belward, A. S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633), 418-422.]
|-
|Screenshot
|[[File:GFMREWM.png|1000px|thumb|centre|]]
|-
|style="background-color: grey;"| Properties
|style="background-color: grey;"|
|-
|Available variables
| Reference Water Mask
|-
|Available accumulations
|
|-
|Available interpolation algorithms
|
|-
|Available filters
|
|-
|Spatial aggregations
|-
|}

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Observed Water Extent (eng)

2022-08-04T09:52:45Z

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Observed Flood Extent (eng)

2022-08-04T09:34:42Z

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Exclusion Mask (eng)

2022-08-04T09:32:38Z

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Affected Population (eng)

2022-08-04T09:25:29Z

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Affected Landcover (eng)

2022-08-04T09:19:41Z

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Advisory Flags (eng)

2022-08-04T08:52:42Z

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