NASA's goal in Earth science is to observe, understand, and model the Earth system to discover how it is changing, to better predict change, and to understand the consequences for life on Earth. The Applied Sciences Program, within the Earth Science Division of the NASA Science Mission Directorate, serves individuals and organizations around the globe by expanding and accelerating societal and economic benefits derived from Earth science, information, and technology research and development.

The Prediction Of Worldwide Energy Resources (POWER) Project, funded through the Applied Sciences Program at NASA Langley Research Center, gathers NASA Earth observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in energy development, building energy efficiency, and supporting agriculture projects.

The POWER project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly, and climatology. The POWER data archive provides data at the native resolution of the source products. The data is updated nightly to maintain near real time availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER services catalog consists of a series of RESTful Application Programming Interfaces, geospatial enabled image services, and web mapping Data Access Viewer. These three service offerings support data discovery, access, and distribution to the project’s user base as ARD and as direct application inputs to decision support tools.

The latest data version update includes hourly-based source ARD, in addition to enhanced daily, monthly, annual, and climatology data. The daily time series for meteorology is available from 1981, while solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning from 1984 for meteorology and from 2001 for solar-based parameters. The hourly data equips users with the ARD needed to model building system energy performance, providing information directly amenable to decision support tools introducing the industry standard EnergyPlus Weather file format.

The POWER Project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly (by year 12 months + annual averages), and climatology. The POWER Data Archive provides data at the native resolution of the source data products. The data is updated nightly to maintain Near Real Time (NRT) availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER Project targets three specific user communities: Renewable Energy (RE), Sustainable Buildings (SB), and Agroclimatology (AG). The POWER Projects provides community specific parameters, output formats, naming conventions, and units that are commonly employed by each user community. The POWER Services Catalog consists of a series of RESTful Application Programming Interfaces (API), geospatial enabled image services, and a web mapping Data Access Viewer (DAV). These three different service offerings support data discovery, access, and distribution to our user base as ARD and as direct application inputs to decision support tools.

The Prediction Of Worldwide Energy Resource (POWER) Project gathers NASA Earth Observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access, and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in renewable energy development, building energy efficiency, and agriculture sustainability. POWER is funded through the NASA Earth Action Program within the Earth Science Mission Directorate at NASA Langley Research Center (LaRC).---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------This annual meteorology service provides time-enabled global Analysis Ready Data (ARD) parameters from 1981 to 2023 for POWER’s communities. Time Interval: AnnualTime Extent: 1981/01/01 to 2023/12/31Time Standard: Local Sidereal Time (LST)Grid Size: 0.5 x 0.5 DegreeProjection: GCS WGS84Extent: GlobalSource: NASA Prediction Of Worldwide Energy Resources (POWER)For questions or issues please email: larc-power-project@mail.nasa.govMeteorology Data Sources:NASA's GMAO MERRA-2 archive (Jan. 1, 1981 – Dec. 31, 2023)Meteorology Data Parameters:CDD10 (Cooling Degree Days Above 10 C): The daily accumulation of degrees when the daily mean temperature is above 10 degrees Celsius.CDD18_3 (Cooling Degree Days Above 18.3 C): The daily accumulation of degrees when the daily mean temperature is above 18.3 degrees Celsius.DISPH (Zero Plane Displacement Height): The height at which the mean velocity is zero due to large obstacles such as buildings/canopy.EVLAND (Evaporation Land): The evaporation over land at the surface of the earth.EVPTRNS (Evapotranspiration Energy Flux): The evapotranspiration energy flux at the surface of the earth.FROST_DAYS (Frost Days): A frost day occurs when the 2m temperature cools to the dew point temperature and both are less than 0 C or 32 F.GWETTOP (Surface Soil Wetness): The percent of soil moisture a value of 0 indicates a completely water-free soil and a value of 1 indicates a completely saturated soil; where surface is the layer from the surface 0 cm to 5 cm below grade.HDD10 (Heating Degree Days Below 10 C): The daily accumulation of degrees when the daily mean temperature is below 10 degrees Celsius.HDD18_3 (Heating Degree Days Below 18.3 C): The daily accumulation of degrees when the daily mean temperature is below 15.3 degrees Celsius.PBLTOP (Planetary Boundary Layer Top Pressure): The pressure at the top of the planet boundary layer.PRECSNOLAND_SUM (Snow Precipitation Land Sum): The snow precipitation sum over land at the surface of the earth.PRECTOTCORR_SUM (Precipitation Corrected Sum): The bias corrected sum of total precipitation at the surface of the earth.PS (Surface Pressure): The average of surface pressure at the surface of the earth.QV10M (Specific Humidity at 10 Meters): The ratio of the mass of water vapor to the total mass of air at 10 meters (kg water/kg total air).QV2M (Specific Humidity at 2 Meters): The ratio of the mass of water vapor to the total mass of air at 2 meters (kg water/kg total air).RH2M (Relative Humidity at 2 Meters): The ratio of actual partial pressure of water vapor to the partial pressure at saturation, expressed in percent.T10M (Temperature at 10 Meters): The air (dry bulb) temperature at 10 meters above the surface of the earth.T2M (Temperature at 2 Meters): The average air (dry bulb) temperature at 2 meters above the surface of the earth.T2MDEW (Dew/Frost Point at 2 Meters): The dew/frost point temperature at 2 meters above the surface of the earth.T2MWET (Wet Bulb Temperature at 2 Meters): The adiabatic saturation temperature which can be measured by a thermometer covered in a water-soaked cloth over which air is passed at 2 meters above the surface of the earth.TO3 (Total Column Ozone): The total amount of ozone in a column extending vertically from the earth's surface to the top of the atmosphere.TQV (Total Column Precipitable Water): The total atmospheric water vapor contained in a vertical column of unit cross-sectional area extending from the surface to the top of the atmosphere.TS (Earth Skin Temperature): The average temperature at the earth's surface.WD10M (Wind Direction at 10 Meters): The average of the wind direction at 10 meters above the surface of the earth.WD2M (Wind Direction at 2 Meters): The average of the wind direction at 2 meters above the surface of the earth.WD50M (Wind Direction at 50 Meters): The average of the wind direction at 50 meters above the surface of the earth.WS10M (Wind Speed at 10 Meters): The average of wind speed at 10 meters above the surface of the earth.WS2M (Wind Speed at 2 Meters): The average of wind speed at 2 meters above the surface of the earth.WS50M (Wind Speed at 50 Meters): The average of wind speed at 50 meters above the surface of the earth.

The Prediction Of Worldwide Energy Resource (POWER) Project gathers NASA Earth Observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access, and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in renewable energy development, building energy efficiency, and agriculture sustainability. POWER is funded through the NASA Earth Action Program within the Earth Science Mission Directorate at NASA Langley Research Center (LaRC).---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------This monthly meteorology service provides time-enabled global Analysis Ready Data (ARD) parameters from 1981 to 2023 for POWER’s communities. Time Interval: MonthlyTime Extent: 1981/01/01 to 2023/12/31Time Standard: Local Sidereal Time (LST)Grid Size: 0.5 x 0.5 DegreeProjection: GCS WGS84Extent: GlobalSource: NASA Prediction Of Worldwide Energy Resources (POWER)For questions or issues please email: larc-power-project@mail.nasa.govMeteorology Data Sources:NASA's GMAO MERRA-2 archive (Jan. 1, 1981 – Dec. 31, 2021)Meteorology Data Parameters:CDD10 (Cooling Degree Days Above 10 C): The daily accumulation of degrees when the daily mean temperature is above 10 degrees Celsius.CDD18_3 (Cooling Degree Days Above 18.3 C): The daily accumulation of degrees when the daily mean temperature is above 18.3 degrees Celsius.DISPH (Zero Plane Displacement Height): The height at which the mean velocity is zero due to large obstacles such as buildings/canopy.EVLAND (Evaporation Land): The evaporation over land at the surface of the earth.EVPTRNS (Evapotranspiration Energy Flux): The evapotranspiration energy flux at the surface of the earth.FROST_DAYS (Frost Days): A frost day occurs when the 2m temperature cools to the dew point temperature and both are less than 0 C or 32 F.GWETTOP (Surface Soil Wetness): The percent of soil moisture a value of 0 indicates a completely water-free soil and a value of 1 indicates a completely saturated soil; where surface is the layer from the surface 0 cm to 5 cm below grade.HDD10 (Heating Degree Days Below 10 C): The daily accumulation of degrees when the daily mean temperature is below 10 degrees Celsius.HDD18_3 (Heating Degree Days Below 18.3 C): The daily accumulation of degrees when the daily mean temperature is below 15.3 degrees Celsius.PBLTOP (Planetary Boundary Layer Top Pressure): The pressure at the top of the planet boundary layer.PRECSNOLAND_SUM (Snow Precipitation Land Sum): The snow precipitation sum over land at the surface of the earth.PRECTOTCORR_SUM (Precipitation Corrected Sum): The bias corrected sum of total precipitation at the surface of the earth.PS (Surface Pressure): The average of surface pressure at the surface of the earth.QV10M (Specific Humidity at 10 Meters): The ratio of the mass of water vapor to the total mass of air at 10 meters (kg water/kg total air).QV2M (Specific Humidity at 2 Meters): The ratio of the mass of water vapor to the total mass of air at 2 meters (kg water/kg total air).RH2M (Relative Humidity at 2 Meters): The ratio of actual partial pressure of water vapor to the partial pressure at saturation, expressed in percent.T10M (Temperature at 10 Meters): The air (dry bulb) temperature at 10 meters above the surface of the earth.T2M (Temperature at 2 Meters): The average air (dry bulb) temperature at 2 meters above the surface of the earth.T2MDEW (Dew/Frost Point at 2 Meters): The dew/frost point temperature at 2 meters above the surface of the earth.T2MWET (Wet Bulb Temperature at 2 Meters): The adiabatic saturation temperature which can be measured by a thermometer covered in a water-soaked cloth over which air is passed at 2 meters above the surface of the earth.TO3 (Total Column Ozone): The total amount of ozone in a column extending vertically from the earth's surface to the top of the atmosphere.TQV (Total Column Precipitable Water): The total atmospheric water vapor contained in a vertical column of unit cross-sectional area extending from the surface to the top of the atmosphere.TS (Earth Skin Temperature): The average temperature at the earth's surface.WD10M (Wind Direction at 10 Meters): The average of the wind direction at 10 meters above the surface of the earth.WD2M (Wind Direction at 2 Meters): The average of the wind direction at 2 meters above the surface of the earth.WD50M (Wind Direction at 50 Meters): The average of the wind direction at 50 meters above the surface of the earth.WS10M (Wind Speed at 10 Meters): The average of wind speed at 10 meters above the surface of the earth.WS2M (Wind Speed at 2 Meters): The average of wind speed at 2 meters above the surface of the earth.WS50M (Wind Speed at 50 Meters): The average of wind speed at 50 meters above the surface of the earth.

The Prediction Of Worldwide Energy Resource (POWER) Project gathers NASA Earth Observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access, and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in renewable energy development, building energy efficiency, and agriculture sustainability. POWER is funded through the NASA Earth Action Program within the Earth Science Mission Directorate at NASA Langley Research Center (LaRC).---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------This monthly radiation service provides time-enabled global Analysis Ready Data (ARD) parameters from 1984 to 2023 for the NASA POWER Project's communities.Time Interval: MonthlyTime Extent: 1984/01/01 to 2023/12/31Time Standard: Local Sidereal Time (LST)Grid Size: 1.0 X 1.0 DegreeProjection: GCS WGS84Extent: GlobalSource: NASA Prediction Of Worldwide Energy Resources (POWER)Radiation Data Sources:NASA's GEWEX/SRB release 4.0 archive (Jan. 1, 1984 – Dec. 31, 2000)NASA's CERES SYN1deg (Jan. 1, 2001 – Dec. 31, 2021)For questions or issues please email: larc-power-project@mail.nasa.govRadiation Data Parameters:ALLSKY_KT (All Sky Insolation Clearness Index): A fraction representing clearness of the atmosphere; the all sky insolation that is transmitted through the atmosphere to strike the surface of the earth divided by the average of top of the atmosphere total solar irradiance incident.ALLSKY_SFC_LW_DWN (All Sky Surface Longwave Downward Irradiance): The downward thermal infrared irradiance under all sky conditions reaching a horizontal plane the surface of the earth. Also known as Horizontal Infrared Radiation Intensity from Sky.ALLSKY_SFC_LW_UP (All Sky Surface Longwave Upward Irradiance): The upward thermal infrared irradiance under all sky conditions.ALLSKY_SFC_PAR_TOT (All Sky Surface PAR Total): The total Photosynthetically Active Radiation (PAR) incident on a horizontal plane at the surface of the earth under all sky conditions.ALLSKY_SFC_SW_DIFF (All Sky Surface Shortwave Diffuse Irradiance): The diffuse (light energy scattered out of the direction of the sun) solar irradiance incident on a horizontal plane at the surface of the earth under all sky conditions.ALLSKY_SFC_SW_DNI (All Sky Surface Shortwave Downward Direct Normal Irradiance): The direct solar irradiance incident to a horizontal plane normal (perpendicular) to the direction of the sun's position under all sky conditions.ALLSKY_SFC_SW_DWN (All Sky Surface Shortwave Downward Irradiance): The total solar irradiance incident (direct plus diffuse) on a horizontal plane at the surface of the earth under all sky conditions. An alternative term for the total solar irradiance is the "Global Horizontal Irradiance" or GHI.ALLSKY_SFC_SW_UP (All Sky Surface Shortwave Upward Irradiance): The upward shortwave irradiance under all sky conditions.ALLSKY_SFC_UV_INDEX (All Sky Surface UV Index): The ultraviolet radiation exposure index.ALLSKY_SFC_UVA (All Sky Surface UVA Irradiance): The ultraviolet A (UVA 315nm-400nm) irradiance under all sky conditions.ALLSKY_SFC_UVB (All Sky Surface UVB Irradiance): The ultraviolet B (UVB 280nm-315nm) irradiance under all sky conditions.ALLSKY_SRF_ALB (All Sky Surface Albedo): The all sky rate of reflectivity of the earth's surface; the ratio of the solar energy reflected by the surface of the earth compared to the total solar energy incident reaching the surface of the earth.AOD_55 (Aerosol Optical Depth 55): The optical thickness at 0.55 um measured vertically; the component of the atmosphere to quantify the removal of radiant energy from an incident beam.AOD_55_ADJ (Adjusted Aerosol Optical Depth 55): The adjusted optical thickness at 0.55 um measured vertically; the component of the atmosphere to quantify the removal of radiant energy from an incident beam.CLOUD_AMT (Cloud Amount): The average percent of cloud amount during the temporal period.CLOUD_AMT_DAY (Cloud Amount at Daytime): The average percent of cloud amount during daylight.CLOUD_AMT_NIGHT (Cloud Amount at Nighttime): The average percent of cloud amount during nighttime.CLOUD_OD (Cloud Optical Visible Depth): The vertical optical thickness between the top and bottom of a cloud.CLRSKY_DAYS (Clear Sky Day): The number of Clear Sky Days if the daytime cloud amount is less than 10 percent.CLRSKY_KT (Clear Sky Insolation Clearness Index): A fraction representing clearness of the atmosphere; the clear sky insolation that is transmitted through the atmosphere to strike the surface of the earth divided by the average of top of the atmosphere total solar irradiance incident.CLRSKY_SFC_LW_DWN (Clear Sky Surface Longwave Downward Irradiance): The downward thermal infrared irradiance under clear sky conditions reaching a horizontal plane the surface of the earth. Also known as Horizontal Infrared Radiation Intensity from Sky.CLRSKY_SFC_LW_UP (Clear Sky Surface Longwave Upward Irradiance): The upward thermal infrared irradiance under clear sky conditions.CLRSKY_SFC_PAR_TOT (Clear Sky Surface PAR Total): The total Photosynthetically Active Radiation (PAR) incident on a horizontal plane at the surface of the earth under clear sky conditions.CLRSKY_SFC_SW_DIFF (Clear Sky Surface Shortwave Downward Diffuse Horizontal Irradiance): The diffuse (light energy scattered out of the direction of the sun) solar irradiance incident on a horizontal plane at the surface of the earth under clear sky conditions.CLRSKY_SFC_SW_DNI (Clear Sky Surface Shortwave Downward Direct Normal Irradiance): The direct solar irradiance incident to a horizontal plane normal (perpendicular) to the direction of the sun's position under clear sky conditions.CLRSKY_SFC_SW_DWN (Clear Sky Surface Shortwave Downward Irradiance): The total solar irradiance incident (direct plus diffuse) on a horizontal plane at the surface of the earth under clear sky conditions. An alternative term for the total solar irradiance is the "Global Horizontal Irradiance" or GHI.CLRSKY_SFC_SW_UP (Clear Sky Surface Shortwave Upward Irradiance): The upward shortwave irradiance under clearsky conditions.CLRSKY_SRF_ALB (Clear Sky Surface Albedo): The clear sky rate of reflectivity of the earth's surface; the ratio of the solar energy reflected by the surface of the earth compared to the total solar energy incident reaching the surface of the earth.MIDDAY_INSOL (Midday Insolation Incident): The total amount of solar irradiance (i.e. direct plus diffuse) incident on a horizontal plane at the earth's surface during the solar noon hour midday period.PW (Precipitable Water): The total atmospheric water vapor contained in a vertical column of the atmosphere.TOA_SW_DNI (Top-Of-Atmosphere Shortwave Direct Normal Radiation): The total solar irradiance incident (direct plus diffuse) on a horizontal plane where oriented to the sun's position at the top of the atmosphere (extraterrestrial radiation).TOA_SW_DWN (Top-Of-Atmosphere Shortwave Downward Irradiance): The total solar irradiance incident (direct plus diffuse) on a horizontal plane at the top of the atmosphere (extraterrestrial radiation).TS_ADJ (Earth Skin Temperature Adjusted): The adjusted average temperature at the earth's surface.

The Prediction Of Worldwide Energy Resource (POWER) Project gathers NASA Earth Observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access, and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in renewable energy development, building energy efficiency, and agriculture sustainability. POWER is funded through the NASA Earth Action Program within the Earth Science Mission Directorate at NASA Langley Research Center (LaRC).---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------This annual radiation service provides time-enabled global Analysis Ready Data (ARD) parameters from 1984 to 2023 for POWER's communities.Time Interval: AnnualTime Extent: 1984/01/01 to 2023/12/31Time Standard: Local Sidereal Time (LST)Grid Size: 1.0 X 1.0 DegreeProjection: GCS WGS84Extent: GlobalSource: NASA Prediction Of Worldwide Energy Resources (POWER)For questions or issues please email: larc-power-project@mail.nasa.govRadiation Data Sources:NASA's GEWEX/SRB release 4.0 archive (Jan. 1, 1984 – Dec. 31, 2000)NASA's CERES SYN1deg (Jan. 1, 2001 – Dec. 31, 2023)Radiation Data Parameters:ALLSKY_KT (All Sky Insolation Clearness Index): A fraction representing clearness of the atmosphere; the all sky insolation that is transmitted through the atmosphere to strike the surface of the earth divided by the average of top of the atmosphere total solar irradiance incident.ALLSKY_SFC_LW_DWN (All Sky Surface Longwave Downward Irradiance): The downward thermal infrared irradiance under all sky conditions reaching a horizontal plane the surface of the earth. Also known as Horizontal Infrared Radiation Intensity from Sky.ALLSKY_SFC_LW_UP (All Sky Surface Longwave Upward Irradiance): The upward thermal infrared irradiance under all sky conditions.ALLSKY_SFC_PAR_TOT (All Sky Surface PAR Total): The total Photosynthetically Active Radiation (PAR) incident on a horizontal plane at the surface of the earth under all sky conditions.ALLSKY_SFC_SW_DIFF (All Sky Surface Shortwave Diffuse Irradiance): The diffuse (light energy scattered out of the direction of the sun) solar irradiance incident on a horizontal plane at the surface of the earth under all sky conditions.ALLSKY_SFC_SW_DNI (All Sky Surface Shortwave Downward Direct Normal Irradiance): The direct solar irradiance incident to a horizontal plane normal (perpendicular) to the direction of the sun's position under all sky conditions.ALLSKY_SFC_SW_DWN (All Sky Surface Shortwave Downward Irradiance): The total solar irradiance incident (direct plus diffuse) on a horizontal plane at the surface of the earth under all sky conditions. An alternative term for the total solar irradiance is the "Global Horizontal Irradiance" or GHI.ALLSKY_SFC_SW_UP (All Sky Surface Shortwave Upward Irradiance): The upward shortwave irradiance under all sky conditions.ALLSKY_SFC_UV_INDEX (All Sky Surface UV Index): The ultraviolet radiation exposure index.ALLSKY_SFC_UVA (All Sky Surface UVA Irradiance): The ultraviolet A (UVA 315nm-400nm) irradiance under all sky conditions.ALLSKY_SFC_UVB (All Sky Surface UVB Irradiance): The ultraviolet B (UVB 280nm-315nm) irradiance under all sky conditions.ALLSKY_SRF_ALB (All Sky Surface Albedo): The all sky rate of reflectivity of the earth's surface; the ratio of the solar energy reflected by the surface of the earth compared to the total solar energy incident reaching the surface of the earth.AOD_55 (Aerosol Optical Depth 55): The optical thickness at 0.55 um measured vertically; the component of the atmosphere to quantify the removal of radiant energy from an incident beam.AOD_55_ADJ (Adjusted Aerosol Optical Depth 55): The adjusted optical thickness at 0.55 um measured vertically; the component of the atmosphere to quantify the removal of radiant energy from an incident beam.CLOUD_AMT (Cloud Amount): The average percent of cloud amount during the temporal period.CLOUD_AMT_DAY (Cloud Amount at Daytime): The average percent of cloud amount during daylight.CLOUD_AMT_NIGHT (Cloud Amount at Nighttime): The average percent of cloud amount during nighttime.CLOUD_OD (Cloud Optical Visible Depth): The vertical optical thickness between the top and bottom of a cloud.CLRSKY_DAYS (Clear Sky Day): The number of Clear Sky Days if the daytime cloud amount is less than 10 percent.CLRSKY_KT (Clear Sky Insolation Clearness Index): A fraction representing clearness of the atmosphere; the clear sky insolation that is transmitted through the atmosphere to strike the surface of the earth divided by the average of top of the atmosphere total solar irradiance incident.CLRSKY_SFC_LW_DWN (Clear Sky Surface Longwave Downward Irradiance): The downward thermal infrared irradiance under clear sky conditions reaching a horizontal plane the surface of the earth. Also known as Horizontal Infrared Radiation Intensity from Sky.CLRSKY_SFC_LW_UP (Clear Sky Surface Longwave Upward Irradiance): The upward thermal infrared irradiance under clear sky conditions.CLRSKY_SFC_PAR_TOT (Clear Sky Surface PAR Total): The total Photosynthetically Active Radiation (PAR) incident on a horizontal plane at the surface of the earth under clear sky conditions.CLRSKY_SFC_SW_DIFF (Clear Sky Surface Shortwave Downward Diffuse Horizontal Irradiance): The diffuse (light energy scattered out of the direction of the sun) solar irradiance incident on a horizontal plane at the surface of the earth under clear sky conditions.CLRSKY_SFC_SW_DNI (Clear Sky Surface Shortwave Downward Direct Normal Irradiance): The direct solar irradiance incident to a horizontal plane normal (perpendicular) to the direction of the sun's position under clear sky conditions.CLRSKY_SFC_SW_DWN (Clear Sky Surface Shortwave Downward Irradiance): The total solar irradiance incident (direct plus diffuse) on a horizontal plane at the surface of the earth under clear sky conditions. An alternative term for the total solar irradiance is the "Global Horizontal Irradiance" or GHI.CLRSKY_SFC_SW_UP (Clear Sky Surface Shortwave Upward Irradiance): The upward shortwave irradiance under clearsky conditions.CLRSKY_SRF_ALB (Clear Sky Surface Albedo): The clear sky rate of reflectivity of the earth's surface; the ratio of the solar energy reflected by the surface of the earth compared to the total solar energy incident reaching the surface of the earth.MIDDAY_INSOL (Midday Insolation Incident): The total amount of solar irradiance (i.e. direct plus diffuse) incident on a horizontal plane at the earth's surface during the solar noon hour midday period.PW (Precipitable Water): The total atmospheric water vapor contained in a vertical column of the atmosphere.TOA_SW_DNI (Top-Of-Atmosphere Shortwave Direct Normal Radiation): The total solar irradiance incident (direct plus diffuse) on a horizontal plane where oriented to the sun's position at the top of the atmosphere (extraterrestrial radiation).TOA_SW_DWN (Top-Of-Atmosphere Shortwave Downward Irradiance): The total solar irradiance incident (direct plus diffuse) on a horizontal plane at the top of the atmosphere (extraterrestrial radiation).TS_ADJ (Earth Skin Temperature Adjusted): The adjusted average temperature at the earth's surface.

In this dataset the anther's analysis is based on data from NREL about Solar & Wind energy generation by operation areas.

NASA Prediction of Worldwide Energy Resources

Solar

Monthly averages for global horizontal radiation over 22-year period (Jul 1983

• Jun 2013)

Wind

Monthly average wind speed at 50m above the surface of earth over a 30-year period (Jan 1984 - Dec 2013)Year: Averaged Over 10 to 15 years

COA = central operating area.

EOA = eastern operating area.

SOA = southern operating area.

WOA = western operating area. Source: NRELSource Link

The Prediction Of Worldwide Energy Resource (POWER) Project gathers NASA Earth Observation data and parameters related to the fields of surface solar irradiance and meteorology to serve out to the public in several free, easy-to-access and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in renewable energy development, building energy efficiency, and supporting agriculture projects. POWER is funded through the NASA Applied Sciences Program within the Earth Science Mission Directorate at NASA Langley Research Center (LaRC). ---------------------------------------------------------------------------------------------------------------------------------------------------------------- Time Interval: Yearly Time Extent: 1981 to 2021 Time Standard: Coordinated Universal Time (UTC) Grid Size: The data is vectorized from a 0.5° x 0.5° latitude and longitude global grid raster. Projection: GCS WGS84 Extent: Global Source: NASA Prediction Of Worldwide Energy Resources (POWER) For questions or issues please email: larc-power-project@mail.nasa.gov Data Source: NASA's GMAO MERRA-2 archive (Jan. 1, 1981 – Dec. 31, 2021) Building Climate Thermal Moisture Zones: This is a feature service that contains 4-Year Rolling Building Climate Thermal Zones for all 4-year periods between 1981 to 2021. Building Climate Thermal Zones are regions of the earth that have the same general climate characteristics based upon climatologically averaged heating and cooling degree days. The thermal zone values range from 0 (hottest) to 8 (coldest). The Thermal Zones are derived from the POWER project's Building Climate Thermal Zones Version 2 Application Programming Interface (API) service and are combined via a python framework. Building Climate Thermal Moisture Zones: This is a feature service that contains 4-Year Rolling Building Climate Thermal Moisture Zones for all 4-year periods between 1981 to 2021. Building Climate Thermal Moisture Zones are regions of the earth that have the same general climate characteristics based upon climatologically averaged heating and cooling degree days and moisture. The thermal zone values range from 0 (hottest) to 8 (coldest). The moisture zones are based upon precipitation data and are characterized as marine, dry, or humid with sub-zones designated as C, B, or A. Additionally, humid zones end with a "1" (example: Zone 5, Humid is "51"), dry zones end with a "2" (example: Zone 1, Dry is "12"), and marine zones end with a "3". The Building Climate Thermal Moisture Zones are derived from the POWER project's Thermal Zones Version 2 Application Programming Interface (API) service and are combined via a python framework.

This dataset comprises Typical Solar Years (TSYs) and Typical Wind Years (TWYs) for the efficient assessment of PV system and wind turbine performance for over 2,000 locations across the U.S. TSYs and TWYs are single synthetic years generated from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER) data spanning from 2001 to 2022. These synthetic years represent the long-term average solar and wind resource conditions of a location, respectively. The POWER solar data is derived from satellite observations and has a spatial resolution of 1 degree * 1 degree (latitude/longitude). The meteorological variables are sourced from NASA's Goddard Earth Observing System (GEOS) Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) assimilation model, which features a spatial resolution of 1/2 degree * 5/8 degree (latitude/longitude). The methods for creating TSYs and TWYs are adapted from the Sandia method. Specifically, the weights assigned to different weather parameters have been adjusted, and the final selection step has been omitted. For TSYs, a weight of 0.7 is assigned to daily cumulative GHI, and 0.3 is assigned to daily cumulative DNI. For TWYs, weights of 0.2, 0.2, and 0.6 are assigned to daily median zonal wind speed, daily median meridional wind speed, and daily 0.75 quantile wind speed, respectively. These weights have been optimized based on the simulated solar PV system and wind turbine outputs. 12 representative months are then selected based on their Finkelstein-Schafer (FS) statistics and concatenated into a synthetic year. The paper describing our methodology has been published in Applied Energy and is available via the "Project Publication" resource link below. The TSYs and TWYs are provided for the centroids of all Public Use Microdata Areas (PUMAs) in the U.S. PUMAs are non-overlapping statistical geographic areas that partition each state or equivalent entity into regions containing no fewer than 100,000 people each. The 2,378 PUMAs collectively cover the entire U.S. A file named "PUMA information.csv" is included with the dataset, containing the PUMA number, PUMA name, latitude, longitude, elevation, and time zone of all PUMA centroids. Users can reference this file to find the PUMAs corresponding to their locations of interest. To accommodate different user communities, the data is provided in three formats. The TSYs are available in EPW and SAM weather file formats, while the TWYs are available in EPW, SAM weather file, and CSV formats. The EPW format, developed by the U.S. Department of Energy, is a de facto standard for weather data in building energy modeling and is compatible with various building energy modeling programs, including EnergyPlus, ESP-r, and IESVE. The SAM weather file format is designed for the System Advisor Model (SAM), a renewable energy project evaluation tool developed by the National Renewable Energy Laboratory (NREL). If you use this dataset in your research, please consider citing our paper: Zeng, Z., Stackhouse, P., Kim, J.-H. (Jeannie), & Muehleisen, R. T. (2025). Development of typical solar years and typical wind years for efficient assessment of renewable energy systems across the U.S. Applied Energy, 377, 124698. https://doi.org/10.1016/j.apenergy.2024.124698.

About Datasets

These datasets contain historical energy consumption data for two buildings from 2016 to 2019. The data is provided in 1-hour intervals. It also has other variables such as weather variables and holidays. The historical data was obtained through smart meters located in each of the buildings and the weather variables were obtained through NASA Prediction Of Worldwide Energy Resources (POWER).

Parameters Description

ENERGY - Energy Consumption (kWh) HDD18_3 - Heating Degree Days below 18.3 °C CDD0 - Cooling Degree Days above 0 °C CDD10 - Cooling Degree Days above 10 °C PRECTOT - Precipitation (mm/hour) RH2M - Relative Humidity at 2 m (%) T2M - Temperature at 2 m (C) T2M_MIN - Minimum Temperature at 2 m (C) T2M_MAX - Maximum Temperature at 2 m (C) ALLSKY - All-Sky Surface Longwave Downward Irradiance (W/m^2) HOLIDAY - Holidays in Spain

This dataset is used in a study (Lee et al. 2021) in which we compared total aerosol forecasts in the Middle East from three publicly available models, from NASA and NOAA in the U.S. and from CAMS in Europe. We used forecasts issued twice daily during all of 2018 and 2019. From each model run we validated forecasts of total aerosol optical depth at 550 nm (AOD550) against observations made from 20 ground-based AERONET stations across the region. We found that the NASA GEOS-5 model performed the best overall in the Middle East, the CAMS NRT model was second-best, and the NOAA NGAC model generally performed poorly. Because dust storms are a relatively common phenomenon in the Middle East and bring significant disruptions both to solar power generation and to society in general, we also examined how well the three forecast models performed during two dust storm events.

GEOS-5 model forecast data: 14.8 GB total, .nc4 format, models initialized at 00 & 12 UTC daily from 1 January 2018 to 31 December 2019, with 1-hourly forecasts (out to 240 hours for 00 UTC cycles, and 120 hours for 12 UTC cycles), 105 latitude points (16.0 degree N to 42.0 degree N, 0.25 degree grid spacing) x 135 longitude points (24.0625 degree E to 65.9375 degree E, 0.3125 degree grid spacing). One file per forecast valid time. Variable: Total aerosol optical extinction at 550 nm. Recent GEOS-5 data can be freely downloaded from https://www.nccs.nasa.gov/services/data-collections/coupled-products/geos5-forecast.

NGAC model forecast data: 1.1 GB total, .grib2 format, models initialized at 00 & 12 UTC daily from 1 January 2018 to 31 December 2019, with 3-hourly forecasts out to 120 hours, 27 latitude points (16.0 degree N to 42.0 degree N, 1.0 degree grid spacing) x 43 latitude points (24.0 degree E to 66.0 degree E, 1.0 degree grid spacing). One file per forecast cycle (contains all lead times). Variable: Total aerosol optical depth at 550 nm. Limited NGAC data can be freely downloaded from https://www.nco.ncep.noaa.gov/pmb/products/ngac.

CAMS NRT model forecast data: 559 GB total, netCDF format, models initialized at 00 & 12 UTC daily from 1 January 2018 to 31 December 2019, with 1-hourly forecasts out to 120 hours, global data at 0.4 degree grid spacing (i.e., not geographically subsetted). One file per forecast valid time. Variable: Total aerosol optical depth at 550 nm. NOTE: Archived CAMS NRT data are only available publicly every 3 hours, so the in-between times were processed as linear interpolations between surrounding 3-hourly times. File variables and names were also processed to mimic the files that can be downloaded in real-time from CAMS. CAMS data is subject to this license: https://www.regional.atmosphere.copernicus.eu/doc/CAMS_data_license_final.pdf. 3-hourly archived CAMS data can be freely downloaded from https://apps.ecmwf.int/datasets/data/cams-nrealtime/levtype=sfc/.

AERONET observation data: 77 MB total, .csv format, daily-average, 1-hour average, and instantaneous L1.5 data (and L2 data when available) from 20 AERONET sites in the Middle East: Tuz_Golu_3 and IMS-METU-ERDEMLI in Turkey; Nicosia, AgiaMarina_Xyliatou, and CUT-TEPAK in Cyprus; Migal, Technion_Haifa_IL, Wiezmann_Institute, SEDE_BOKER, and Eilat in Israel; Cairo_EMA_2, El_Farafra, and Qena_SVU in Egypt; KAUST_Campus in Saudi Arabia; IASBS in Iran; Shagaya_Park and Kuwait_University in Kuwait; and Mezaira, Masdar_Institute, and DEWA_ResearchCentre in UAE. Date range: 1 January 2018 to 2 January 2020. L1 (raw) data is also provided from 1 to 31 July 2018 at Mezaira, and from 20 to 30 April 2018 at Shagaya_Park to span two dust storm events. Variables: Total aerosol optical depth at 550 nm and Angstrom exponent. AERONET data can be freely downloaded from https://aeronet.gsfc.nasa.gov/new_web/data.html.

Lunar Energy Harvesting Market Outlook

The global lunar energy harvesting market size is projected to grow from USD 1.5 billion in 2023 to USD 6.7 billion by 2032, exhibiting a robust CAGR of 18.2% during the forecast period. This exponential growth can be attributed to the increasing interest and investments in space exploration and sustainable energy solutions for lunar missions and potential lunar colonization.

One of the primary growth factors driving the lunar energy harvesting market is the rising number of planned lunar missions by government space agencies such as NASA, ESA, and Roscosmos. These missions require reliable and sustainable energy sources to support long-term operations on the moon. Additionally, advancements in technologies such as solar power and nuclear power are making it feasible to generate energy in the harsh lunar environment. With the Artemis program aiming to return humans to the moon by 2024 and establish a sustainable presence by the end of the decade, the demand for lunar energy solutions is set to surge.

Another significant factor contributing to market growth is the increasing involvement of private players and commercial entities in space exploration. Companies like SpaceX, Blue Origin, and others are actively working on lunar missions and infrastructure. These private enterprises are not only augmenting government efforts but also introducing innovative technologies and business models that drive the commercialization of lunar energy harvesting. The collaboration between public and private sectors is expected to lead to significant advancements and cost efficiencies, further bolstering market growth.

Moreover, the exploration of lunar resources, such as water ice and regolith, is expected to play a crucial role in energy harvesting on the moon. Water ice can be converted into hydrogen and oxygen for fuel, while regolith can be used in regolith-based power generation systems. These resources present opportunities for sustainable energy production, which is essential for long-duration missions and potential lunar habitation. The technological advancements in resource extraction and utilization are poised to significantly impact the lunar energy harvesting market.

Space Resource Utilization is becoming an integral aspect of lunar missions, as it offers the potential to significantly reduce the cost and complexity of transporting materials from Earth. By leveraging the moon's natural resources, such as water ice and regolith, space missions can produce essential supplies like fuel and building materials directly on the lunar surface. This approach not only supports long-duration missions but also lays the groundwork for sustainable lunar habitation. The ability to utilize in-situ resources is expected to revolutionize the economics of space exploration, making it more feasible for both government and commercial entities to establish a permanent presence on the moon. As technology advances, the efficiency and effectiveness of space resource utilization will continue to improve, playing a pivotal role in the future of lunar energy harvesting and colonization.

Regionally, North America is anticipated to dominate the lunar energy harvesting market due to substantial investments in space exploration and the presence of key players in the region. However, significant growth is also expected in the Asia Pacific region, driven by ambitious space programs in countries like China and India. Europe and other regions are also actively participating in lunar exploration initiatives, contributing to the overall market expansion.

Technology Analysis

The lunar energy harvesting market can be segmented by technology into solar power, nuclear power, regolith-based power, and others. Solar power is one of the most promising technologies for generating energy on the moon. The lunar surface receives abundant sunlight, making solar panels an effective solution for energy harvesting. Advances in photovoltaic technology, such as high-efficiency solar cells and dust-resistant coatings, are enhancing the viability of solar power for lunar missions. The ability to store solar energy in batteries for use during the lunar night is also a critical area of development.

Nuclear power is another crucial technology for lunar energy harvesting, offering a reliable and continuous energy source. Nuclear reactors can provide consistent power output regardless of environmental conditions, making the

This repository contains all necessary raw data as well as the R code used to conduct statistical analysis and create figures of the publication

The effect of crop diversification and season on microbial carbon use efficiency across a European pedoclimatic gradient

Julia Schroeder1*, Alexander König2, Christopher Poeplau1, Tobias Bölscher3, Katharina H.E. Meurer4, Monika Toleikienė5, Marjoleine Hanegraaf6, Annelein Meisner6, Josef Hakl7, Katharina M. Keiblinger 2, Abad Chabbi8, Marjetka Suhadolc9, Anton Govednik9, Erich Inselsbacher2, Heike Knicker10, Laura Gismero Rodríguez10, and Anke M. Herrmann4

1 Thünen Institute of Climate-Smart Agriculture, Braunschweig, Germany
2 University of Natural Resources and Applied Life Sciences Vienna, Department of Forest and Soil Sciences, Institute of Soil Research, Vienna, Austria
3 Université Paris-Saclay, INRAE, AgroParisTech, UMR EcoSys, Palaiseau, France
4 Department of Soil & Environment, Swedish University of Agricultural Sciences - SLU, Uppsala, Sweden
5 Lithuanian Research Centre for Agriculture and Forestry, Akademija, Lithuania
6 Wageningen University & Research, Wageningen Plant Research, Wageningen, Netherlands
7 Czech University of Life Sciences Prague, Czech Republic
8 INRAE Centre de Recherche Nouvelle-Aquitaine-Poitiers, Unité de Recherche Pluridisciplinaire Prairies & Plantes Fourragères, Lusignan, France
9 University of Ljubljana, Biotechnical Faculty, Ljubljana, Slovenia
10 Instituto de la Grasa (IG-CSIC), Sevilla, Spain
* Corresponding author: julia.schroeder@thuenen.de

DOI:

The study aimed to investigate the effect of crop diversification measures (cover crops, ley farming, vegetation stripes) on microbial carbon use efficiency (CUE) and its potential link to SOC accrual in agricultural soils across Europe. The central hypothesis was that the crop diversification treatment results in more efficient microbial use of C, thus enhancing the potential of soils to store C. The effect of treatment was expected to vary between seasons.
Topsoil was sampled from eight experimental crop diversification sites across a pan-European pedoclimatic gradient (Sweden, Netherlands, Lithuania, Czech Republic, Austria, France, Slovenia, and Spain). At five sites, a second sampling was conducted to test the effect of season on CUE (Sweden, Netherlands, France, Slovenia, and Spain). CUE was assessed by the 18O-labelling method. To account for the different experimental layout between sites, a meta-analysis approach was used for statistical analysis. To test for a general pattern of the seasonal variation in CUE across the pedoclimatic gradient, weather data (representing 3-months weather prior sampling) was used to extract seasonal predictors.
For further details, please see the peer-reviewed publication.

The R code was developed under R v4.4.0.

The repository includes the following files:

data:

• EJP-EL_COORD_sites.csv - Provides coordinates, reference system, land use, diversification treatment and year of establishment for the experimental sites (n=8).
• EJP-EL_DATA_sites.csv - Provides texture, TOC and N content, and bulk density data for the experimental sites as provided by site managers (if applicable on plot basis)(n=106).
• EJP-EL_CUE_for_R.csv - Provides assessed per sample observations (n=220)
  WHC: water holding capacity, pH: soil pH measured in 1:5 w/w soil-water solution, TC_perc: total C (%), TIC_perc: total inorganic C (%), TOC_perc: total organic C (%), TN_perc: total N (%), total_DNA_gsoil: total amount of DNA extracted (ug DNA g-1 soil), Cmic_ugC_gsoil: microbial biomass C by CFE-method (ug microbial biomass C g-1 soil), fDNA: unitless conversion factor, Cgrowth_ngC_g_h: microbial growth rate (ng C g-1 soil h-1), Crespiration_ngC_g_h: microbial respiration rate (ng C g-1 soil h-1), CUE: unitless carbon use efficiency, mass_specific_growth_1_perd: mass specific growth rate (1 d-1), turnover_time_d: turnover rate (d), DW/WW_during_incub: ratio of dry weight to wet weight (indicator of water content) during the time of incubation (g g-1); WC_perc_related_to_DW_at_sampling: water content during the time of soil sampling expressed as percentage water refered to dry weight of soil (%)
• weather_data_b4_sampling_FS_nasa_power_mod_incl_radiation.csv - Extracted daily weather data for the respective site coordinates from the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) Prediction of Worldwide Energy Resource (POWER) Project funded through the NASA Earth Science/Applied Science Program, i.e. NASA POWER project, for a 3-months period prior to each sampling event: mean daily air temperature at 2 m (T2M), the bias corrected average of total precipitation at the Earth’s surface (PRECTOTCORR), and the total photosynthetically active radiation incident at the Earth’s surface (ALLSKY_SFC_PAR_TOT).
• Table_S1.xlsx - Provides meta-data of sites including sampling (treatments, depth, number of samples, time), water content at sampling, management (last main crop, cover at sampling, phenological state at sampling, fertilisation dates, type of fertiliser, fertilisation rate, ploughing date, ploughing depth).

Rproj:

• R_project_Schroeder_crop_div_and_season.Rproj - Rproject (load project to work on provided scripts and data)

R scripts:

• 01_EJP-EL_CUE_load_data.R - Loads data from different provided csv and merges them into one data.frame, specifying assignment of categories (e.g. control vs one diversified treatment) and selections for individual analyses (e.g. seasonality (all treatments, 5 sites), EU.gradient (control vs. one diversified treatment, 8 sites)).
• 02_extract_climate_data_sites.R - Extracts climate data from BIOCLIM database (MAT, MAP, Growing season lenght, Köppen Geiger) for site coordinates.
• 03_meta-analysis_crop_diversification_EU_gradient.R - Conducts meta-analysis on EU.gradient selection (control vs diversified treatment, 8 sites) with crop diversification measures as subgroups. Plots results of the meta-analysis. Exports overall test statistics.
• 04_ANOVA_seasonality_effect.R - Plots CUE per site and sampling on seasonality selection. Conducts site-wise ANOVA to test for effects of sampling and treatment on CUE, and whether the effect of treatment varies with sampling (i.e. interaction).
• 05_define_season_by_weather_data_before_sampling.R - Extracts weather data from indicated NASA database for the 3-months period prior to sampling. Calculates seasonality predictors (see M&M of publication to this study for detailed background). Creates table with calculated predictors.
• 06_Driver_Analysis_seasonality.R - Checks for autocorrelation of seasonal predictors. Conducts linear mixed-effects model for driver analysis. Creates figure presenting results of the driver analysis.
• 07_PCA_pedo-climatic_gradient_EU.R - Conducts and plots a principal component analysis to visualise the spread of pedo-climatic properties across sites.
• 08_LMEM_France.R - Conducts a separate linear mixed-effects model statistics to test wehther ley farming is more similar to cropland or grassland (microbial CUE, Cmic, Cgrowth and SOC) at the Lusignan (France) site.

The Prediction of Worldwide Energy Resource (POWER) Project is funded through the NASA Applied Sciences Program within the Earth Science Mission Directorate. The POWER Project supports three user communities with solar and/or meteorological data: 1) Renewable Energy (RE), 2) Sustainable Buildings (SB), and 3) Agroclimatology (AG)POWER Data Sources:The POWER project provides access to community-based Analysis Ready Data (ARD) for meteorology and solar-related parameters, specifically formulated for assessing and designing renewable energy systems.The data is available on at the source models’ native latitude and longitude global grid.Temporal levels include Hourly, Daily, Monthly, Annual, and Climatology. Download options include single point, regional, and global data.Formats include NetCDF, CSV, ASCII, geoJSON, ICASA, & EPW.Meteorological parameters are derived from:NASA's GMAO MERRA-2 archive (Jan. 1, 1981 – 3 Months Behind Near Real Time)NASA's GEOS 5.12.4 FP-IT archive (End of MERRA2 – Near Real Time)Solar parameters are derived from:NASA's GEWEX/SRB release 4.0 archive (Jan. 1, 1984 – Dec. 31, 2000) NASA's CERES SYN1deg (Jan. 1, 2001 – 3 Months Behind Near Real Time)NASA's FLASHFlux (3 Months Behind Near Real Time – Near Real Time)If you have any comments or questions, please do not hesitate to contact us at larc-power-project@mail.nasa.gov

The global Stirling engines market is anticipated to rise from USD 1.27 Billion in 2025 to USD 2.24 Billion by 2033, exhibiting a CAGR of 6.50% during the forecast period. The growing demand for efficient and environmentally friendly energy sources is driving the market growth. Stirling engines offer several advantages, including high efficiency, low emissions, and the ability to use various heat sources. The heating and cooling segment is expected to dominate the market during the forecast period. The increasing demand for energy-efficient heating and cooling systems in residential and commercial buildings is driving the segment's growth. Additionally, government regulations and incentives promoting energy efficiency further support the segment's growth. Key companies operating in the Stirling engines market include Qnergy. (U.S.), United Sun Systems,International (Sweden), Sunpower Inc. (U.S.), Genoastirling S.r.l. (Italy), and others. These companies are involved in the research, development, manufacturing, and marketing of Stirling engines and related technologies. Recent developments include: September 2019: Azelio, Abu Dhabi Future Energy Company (Masdar), and Khalifa University of Science and Technology have formed a partnership to test, demonstrate, and evaluate Azelio's Stirling engine system and integrated thermal energy storage (Tes) for renewable energy production using photovoltaic (PV) and concentrated solar power (CSP), and wind energy, as well as off-grid networ., June 2019: Saab Kockums and the Swedish Defence Materiel Administration (FMV) are preparing to test a prototype of the 'Double-Stirling air-independent propulsion system for the embryonic Swedish future submarine programme.., June 2019: The engineering team at NASA Glenn's Thermal Energy Conversion Branch established a new record for the maximum runtime of a free-piston Stirling engine. Since 2003, Technology Demonstration Converter (TDC) #13, an experimental device, has operated for more than 110,000 hours cumulatively without incident or obvious wear., Sunpower Inc. has continued its research and development activities in combining Stirling engines with solar power systems. Sunpower announced a partnership with a leading solar energy company in early 2023 to build an efficient solar Stirling engine system., For example, in September 2022, Qnergy implemented its first landfill gas-to-power project, creating electrical power while generating voluntary carbon credits in collaboration with Maryland Environmental Services and Maryland Energy Administration., In July 2022, Sterling Generators collaborated with the Moteurs Baudouin. This new cooperation will exploit Baudouin’s traditions and competencies for crafting top-ranking petrol and diesel motors and Sterling Generators., Genoastirling S.r.l has been exploring advanced Stirling engine designs for marine applications as well as defense industries. In 2022, the company won a contract from a major European defense contractor to supply engines for future naval vessels.​, An instance is when China State Shipbuilding disclosed during December 2021 that the company had tested the country’s first large bore engine applicable to submarine propulsion. At maximum capacity, this latest prototype achieved a power of 320 KW, which converted almost 40% of it into useful work, thus making it the most powerful globally., Azelio introduced a new electro-thermal system for multiple commercial settings, having four operations in October 2021. The system enables heat production at between 55-65 C at all times of the day, depending on demand, Qnergy has also expanded its product line and improved its technology to enhance efficiency and reliability levels. For example, Qnergy launched a new range of combined heat and power (CHP) engines targeted at residential and commercial markets in 2021​.. Notable trends are: Growing demand for a low-emissions alternative to IC engines will driving the market growth.

This case follows the evolution of a low pressure system through its life cycle as it moved from Arkansas to Illinois, and provides a good example of the effects of model biases in the AVN model. The errors introduced to the AVN model due to inherent biases resulted in an incorrect prediction of strong cyclogenesis over the Gulf of Mexico.

For more information, see: http://data.eol.ucar.edu/codiac/projs?COMET_CASE_015