Predicted temperature and precipitation values were generated throughout the state of Massachusetts using a stochastic weather generator (SWG) model to develop various climate change scenarios (Steinschneider and Najibi, 2022a). This data release contains temperature and precipitation statistics (SWG_outputTable.csv) derived from the SWG model under the surface warming derived from the RCP 8.5 climate change emissions scenario at 30-year moving averages centered around 2030, 2050, 2070, 2090. During the climate modeling process, extreme precipitation values were also generated by scaling previously published intensity-duration-frequency (IDF) values from the NOAA Atlas 14 database (Perica and others, 2015) by a factor per degree expected warming produced from the SWG model generator (Najibi and others, 2022; Steinschneider and Najibi, 2022b, c). These newly generated IDF values (IDF_outputTable.csv) account for expected changes in extreme precipitation driven by variations in weather associated with climate change throughout the state of Massachusetts. The data presented here were developed in collaboration with the Massachusetts Executive Office of Energy and Environmental Affairs and housed on the Massachusetts climate change clearinghouse webpage (Massachusetts Executive Office of Energy and Environmental Affairs, 2022). References: Massachusetts Executive Office of Energy and Environmental Affairs, 2022, Resilient MA Maps and Data Center at URL https://resilientma-mapcenter-mass-eoeea.hub.arcgis.com/ Najibi, N., Mukhopadhyay, S., and Steinschneider, S., 2022, Precipitation scaling with temperature in the Northeast US: Variations by weather regime, season, and precipitation intensity: Geophysical Research Letters, v. 49, no. 8, 14 p., https://doi.org/10.1029/2021GL097100. Perica, S., Pavlovic, S., St. Laurent, M., Trypaluk, C., Unruh, D., Martin, D., and Wilhite, O., 2015, NOAA Atlas 14 Volume 10 Version 3, Precipitation-Frequency Atlas of the United States, Northeastern States (revised 2019): NOAA, National Weather Service, https://doi.org/10.25923/99jt-a543. Steinschneider, S., and Najibi, N., 2022a, A weather-regime based stochastic weather generator for climate scenario development across Massachusetts: Technical Documentation, Cornell University, https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_WGEN_20220405.pdf. Steinschneider, S., and Najibi, N., 2022b, Future projections of extreme precipitation across Massachusetts—a theory-based approach: Technical Documentation, Cornell University, https://eea-nescaum-dataservices-assets-prd.s3.amazonaws.com/cms/GUIDELINES/FinalTechnicalDocumentation_IDF_Curves_Dec2021.pdf. Steinschneider, S., and Najibi, N., 2022c, Observed and projected scaling of daily extreme precipitation with dew point temperature at annual and seasonal scales across the northeast United States: Journal of Hydrometeorology, v. 23, no. 3, p. 403-419, https://doi.org/10.1175/JHM-D-21-0183.1.

This dataset contains water balance data for each year when winter wheat was grown at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU) research weather station, Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Winter wheat was grown on two large, precision weighing lysimeters, each in the center of a 4.44 ha square field in the 1989-1990, 1991-1992, and 1992-1993 seasons. Irrigation was by linear move sprinkler system. Full irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe to 2.4-m depth in the field. Deficit irrigations were less than full - see crop calendars and irrigation data in these files for details. The weighing lysimeters were used to measure relative soil water storage to 0.05 mm accuracy at 5-minute intervals, and the 5-minute change in soil water storage was used along with precipitation and irrigation amounts to calculate crop evapotranspiration (ET), which is reported at 15-minute intervals. Because the large (3 m by 3 m surface area) weighing lysimeters are better rain gages than are tipping bucket gages, the 15-minute precipitation data are derived for each lysimeter from changes in lysimeter mass. The land slope is <0.3% and flat. The water balance data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost fall, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected. The ET data should be considered to be the best values offered in these datasets. Even though ET data are also presented in the "lysimeter" datasets, the values herein are the result of a more rigorous quality control process. Dew and frost accumulation varies from year to year and seasonally within a year, and it is affected by lysimeter surface condition [bare soil, tillage condition, residue amount and orientation (flat or standing), etc.]. Particularly during winter and depending on humidity and cloud cover, dew and frost accumulation sometimes accounts for an appreciable percentage of total daily ET. These datasets originate from research aimed at determining crop water use (ET), crop coefficients for use in ET-based irrigation scheduling based on a reference ET, crop growth, yield, harvest index, and crop water productivity as affected by irrigation method, timing, amount (full or some degree of deficit), agronomic practices, cultivar, and weather. Prior publications have focused on winter wheat ET, crop coefficients, and crop water productivity. Crop coefficients have been used by ET networks. The data have utility for testing simulation models of crop ET, growth, and yield. Resources in this dataset:Resource Title: 1989 Bushland, TX. West Winter Wheat Evapotranspiration, Irrigation, and Water Balance Data. File Name: 1989_W_Wheat_water_balance.xlsxResource Description: The data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost accumulation, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to precipitation, irrigation, frost and dew accumulation, emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected.Resource Title: 1990 Bushland, TX. West Winter Wheat Evapotranspiration, Irrigation, and Water Balance Data. File Name: 1990_W_Wheat_water_balance.xlsxResource Description: The data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost accumulation, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to precipitation, irrigation, frost and dew accumulation, emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected.Resource Title: 1991 Bushland, TX. East Winter Wheat Evapotranspiration, Irrigation, and Water Balance Data. File Name: 1991_E_Wheat_water_balance.xlsxResource Description: The data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost accumulation, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to precipitation, irrigation, frost and dew accumulation, emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected.Resource Title: 1992 Bushland, TX. East Winter Wheat Evapotranspiration, Irrigation, and Water Balance Data. File Name: 1992_E_Wheat_water_balance.xlsxResource Description: The data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost accumulation, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to precipitation, irrigation, frost and dew accumulation, emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected.Resource Title: 1992 Bushland, TX. West Winter Wheat Evapotranspiration, Irrigation, and Water Balance Data. File Name: 1992_W_Wheat_water_balance.xlsxResource Description: The data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost accumulation, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to precipitation, irrigation, frost and dew accumulation, emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected.Resource Title: 1993 Bushland, TX. West Winter Wheat Evapotranspiration, Irrigation, and Water Balance Data. File Name: 1993_W_Wheat_water_balance.xlsxResource Description: The data consist of 15-minute and daily amounts of evapotranspiration (ET), dew/frost accumulation, precipitation (rain/snow), irrigation, scale counterweight adjustment, and emptying of drainage tanks, all in mm. The values are the result of a rigorous quality control process involving algorithms for detecting dew/frost accumulations, and precipitation (rain and snow). Changes in lysimeter mass due to precipitation, irrigation, frost and dew accumulation, emptying of drainage tanks, counterweight adjustment, maintenance activity, and harvest are accounted for such that ET values are minimally affected.

Meteorological measurements for 2023 at MBL Marshview Farm, Newbury, MA. Sensors conduct measurements every 5 seconds and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.

Year 2017 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.

This excel contains data for Chapter 2 “Precipitation” of the 2017 State of Narragansett Bay & Its Watershed Technical Report (nbep.org). It includes the raw data behind Figure 1, “Annual precipitation at Providence, RI,” (page 64); Figure 2, “Annual precipitation at Worcester, MA,” (page 64); Figure 3, “Annual Palmer Drought Severity Index (PDSI) for Rhode Island,” (page 65); Figure 4, "Annual Palmer Drought Severity Index (PDSI) for Massachusetts," (page 65); Figure 5, "Climate model projection of winter total precipitation in RI or MA to 2100," (page 67); and Figure 6, "Climate model projection of winter annual snowfall in RI or MA to 2100," (page 67). For more information, please reference the Technical Report or contact info@nbep.org. Original figures are available at http://nbep.org/the-state-of-our-watershed/figures/.

Led by the Massachusetts Executive Office of Energy and Environmental Affairs (EEA), in partnership with Cornell University, U.S. Geological Survey and Tufts University, the Massachusetts Climate and Hydrologic Risk Project (Phase 1) has developed new climate change projections for the Commonwealth. These new temperature and precipitation projections are downscaled for Massachusetts at the HUC8 watershed scale using Global Climate Models (GCMs) and a Stochastic Weather Generator (SWG) developed by Cornell University.

Stochastic weather generators provide a computationally efficient and complementary alternative to direct use of GCMs for investigating water system performance under climate stress. These models are configured based on existing meteorological records (i.e., historical weather) and are then used to generate large ensembles of simulated daily weather records that are similar to but not bound by variability in past observations. Once fit to historical data, model parameters can be systematically altered to produce new traces of weather that exhibit a wide range of change in their distributional characteristics, including the intensity and frequency of average and extreme precipitation, heatwaves, and cold spells.

The Phase 1 SWG was developed, calibrated, and validated across all HUC8 watersheds that intersect with the state of Massachusetts. A set of climate change scenarios for those watersheds were generated that only reflect mechanisms of thermodynamic climate change deemed to be most credible. These thermodynamic climate changes are based on the range of temperature projections produced by a set of downscaled GCMs for the region. The temperature and precipitation projections presented in this dashboard reflect a warming scenario linked to the Representation Concentration Pathway (RCP) 8.5, a comparatively high greenhouse gas emissions scenario.

The statistics presented in this series of map layers are expressed as either a percent change or absolute change (see list of layers with units and definitions below). These changes are referenced to baseline values that are calculated based on the median value across the 50 model ensemble members associated with the 0°C temperature change scenario derived from observational data (1950-2013) from Livneh et al. (2015). The temperature projections derived from the downscaled GCMs for the region, which are used to drive the SGW, are averaged across 30 years and centered on a target decade (i.e., 2030, 2050, 2070). Projections for 2090 are averaged across 20 years.Definitions of climate projection metrics (with units of change):Total Precipitation (% change): The average total precipitation within a calendar year. Maximum Precipitation (% change): The maximum daily precipitation in the entire record. Precipitation Depth – 90th Percentile Storm (% change): The 90th percentile of non-zero precipitation. Precipitation Depth –99th Percentile Storm (% change): The 99th percentile of non-zero precipitation. Consecutive Wet Days (# days): The average number of days that exist within a run of 2 or more wet days. Consecutive Dry Days (# days): The average number of days that exist within a model run of 2 or more dry days. Days above 1 inch (# days): The number of days with precipitation greater than 1 inch. Days above 2 inches (# days): The number of days with precipitation greater than 2 inches.Days above 4 inches (# days): The number of days with precipitation greater than 4 inches.Maximum Temperature (°F): The maximum daily average temperature value in the entire recordAverage Temperature (°F): Daily average temperature.Days below 0 °F (# days): The number of days with temperature below 0 °F.Days below 32 °F (# days): The number of days with temperature below 32 °F.Maximum Duration of Coldwaves (# days): Longest duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Average Duration of Coldwaves (# days): Average duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Number of Coldwave Events (# events): Number of instances with ten or more consecutive days with temperature below 20 °F.Number of Coldstress Events (# events): Number of instances when a 3-day moving average of temperature is less than 32 °F. Days above 100 °F (# days): The number of days with temperature above 100 °F.Days above 95 °F (# days): The number of days with temperature above 95 °F.Days above 90 °F (# days): The number of days with temperature above 90 °F.Maximum Duration of Heatwaves (# days): Longest duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Average Duration of Heatwaves (# days): Average duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Number of Heatwave Events (# events): Number of instances with three or more consecutive days with temperature over 90 °F.Number of Heatstress Events (# events): Number of instances when a 3-day moving average of temperature is above 86 °F.Cooling Degree Days (# degree-day): Cooling degree days assume that when the outside temperature is below 65°F, we don't need cooling (air-conditioning) to be comfortable. Cooling degree-days are the difference between the daily temperature mean and 65°F. For example, if the temperature mean is 85°F, we subtract 65 from the mean and the result is 20 cooling degree-days for that day. (Definition adapted from National Weather Service).Heating Degree Days (# degree-day): Heating degree-days assume that when the outside temperature is above 65°F, we don't need heating to be comfortable. Heating degree days are the difference between the daily temperature mean and 65°F. For example, if the mean temperature mean is 25°F, we subtract the mean from 65 and the result is 40 heating degree-days for that day. (Definition adapted from National Weather Service).Growing Degree Days (# degree-day): A growing degree day (GDD) is an index used to express crop maturity. The index is computed by subtracting a base temperature of 50°F from the average of the maximum and minimum temperatures for the day. Minimum temperatures less than 50°F are set to 50, and maximum temperatures greater than 86°F are set to 86. These substitutions indicate that no appreciable growth is detected with temperatures lower than 50° or greater than 86°. (Adapted from National Weather Service).Please see additional information related to this project and dataset in the Climate Change Projection Dashboard on the Resilient MA Maps and Data Center webpage.

This repository holds supplemental figures for the paper "On Strictly Enforced Mass Conservation Constraints for Modeling the Rainfall-Runoff Process". These include distributions of event-based runoff ratios, scatter plots comparing event-based runoff ratios, initial (antecedent) flows, and rainfall totals, and hydrographs of every qualifying "event" that spans both Daymet and NLDAS forcing data.

Sediment fluxes to the seafloor govern the fate of elements and compounds in the ocean and serve as a prerequisite for research on elemental cycling, benthic processes and sediment management strategies. To quantify these fluxes over seafloor areas, it is necessary to scale up sediment mass accumulation rates (MAR) obtained from multiple sample stations. Conventional methods for spatial upscaling involve averaging of data or spatial interpolation. However, these approaches may not be sufficiently precise to account for spatial variations of MAR, leading to poorly constrained regional sediment budgets. Here, we utilize a machine learning approach to scale up porosity and 210Pb data from 145 and 65 stations, respectively, in the Skagerrak. The models predict the spatial distributions by considering several predictor variables that are assumed to control porosity and 210Pb rain rates. The spatial distribution of MAR is based on the predicted porosity and existing sedimentation rate data. Our findings reveal highest MAR and 210Pb rain rates to occur in two parallel belt structures that align with the general circulation pattern in the Skagerrak. While high 210Pb rain rates occur in intermediate water depths, the belt of high MAR is situated closer to the coastlines due to lower porosities at shallow water depths. Based on the spatial distributions, we calculate a total MAR of 34.7 Mt yr-1 and a 210Pb rain rate of 4.7 · 1014 dpm yr-1. By comparing atmospheric to total 210Pb rain rates, we further estimate that 24% of the 210Pb originates from the local atmospheric input, with the remaining 76% being transported laterally into the Skagerrak. The updated MAR in the Skagerrak is combined with literature data on other major sediment sources and sinks to present a tentative sediment budget for the North Sea, which reveals an imbalance with sediment outputs exceeding the inputs. Substantial uncertainties in the revised Skagerrak MAR and the literature data might close this imbalance. However, we further hypothesize that previous estimates of suspended sediment inputs into the North Sea might have been underestimated, considering recently revised and elevated estimates on coastal erosion rates in the surrounding region of the North Sea.

This data set includes the materials required to reproduce the figures and tables presented in the study: "Poleward shift of subtropical highs drives Patagonian glacier mass loss". The data consist of:

ASCII files:

1. SMB-components.MAR3v14.1940-2023.PAT.txt: time series of spatially integrated Patagonian glacier annual surface mass balance (SMB) components in Gt per year from ERA5-forced MARv3.14 at 5 km, statistically downscaled to 500 m resolution (1940-2023). SMB components include SMB, total precipitation (PR), snowfall (SF), rainfall (RA), runoff (RU), total melt (ME), refreezing and retention (RF), total sublimation (SU). Note that MARv3.14 does not account for drifting snow erosion (ER).
2. SMB-components.RACMO2.3p2.1979-2023.PAT.txt: time series of spatially integrated Patagonian glacier annual SMB components in Gt per year from ERA5-forced RACMO2.3p2 at 5.5 km, statistically downscaled to 500 m resolution including precipitation adjustment (1979-2023). SMB components include SMB, total precipitation (PR), snowfall (SF), rainfall (RA), runoff (RU), total melt (ME), refreezing and retention (RF), total sublimation (SU), and drifting snow erosion (ER).

Netcdf files:

1. XXXX.1940-2023.MAR3v14.PAT.0.5km.YY.nc: map of annual SMB component XXXX expressed in kg per m² or mm w.e. per year from ERA5-forced MARv3.14 at 5 km, statistically downscaled to 500 m resolution covering Patagonian glaciers and icefields (1940-2023).
2. XXXX.1979-2023.RACMO2.3p2.PAT.0.5km.YY.nc: map of annual SMB component XXXX expressed in kg per m² or mm w.e. per year from ERA5-forced RACMO2.3p2 at 5.5 km, statistically downscaled to 500 m resolution with precipitation adjustment covering Patagonian glaciers and icefields (1979-2023).

SMB components XXXX include: SMB (smb_rec), total precipitation (precip), snowfall, rainfall (precip - snowfall), runoff, total melt (snowmelt), refreezing and retention (refreeze), total sublimation (subl), and drifting snow erosion (sndiv). Note that MAR3v14 does not account for drifting snow erosion. Annual mean glacier near-surface temperature (t2m) is also available from statistically downscaled MARv3.14 and RACMO2.3p2 at 500 m (ºC).

3. PAT_icemask_lon_lat_0.5km.nc: fractional ice mask ranging from 0 (ice-free) to 1 (fully ice-covered), and longitude/latitude of the 500 m grid.

The projection used for statistical downscaling is Polar Stereographic South (EPSG:3031) with a spatial resolution of 500 m x 500 m.

Additional data: The gridded, daily downscaled SMB data sets from the ERA5-forced MARv3.14 (1940-2023) and RACMO2.3p2 reconstructions (1979-2023) are freely available from the authors upon request and without conditions (contact: bnoel@uliege.be).

Abstract: Patagonian glaciers have been rapidly losing mass in the last two decades, but the driving processes remain poorly known. Here we use two state-of-the-art regional climate models to reconstruct long-term (1940-2023) glacier surface mass balance (SMB), i.e., the difference between precipitation accumulation, surface runoff and sublimation, at about 5 km spatial resolution, further statistically downscaled to 500 m. High-resolution SMB agrees well with in-situ observations and, combined with solid ice discharge estimates, captures recent GRACE/GRACE-FO satellite mass change. Glacier mass loss coincides with a long-term SMB decline (-0.35 Gt yr⁻²), primarily driven by enhanced surface runoff (+0.47 Gt yr⁻²) and steady precipitation. We link these trends to a poleward shift of the subtropical highs favouring warm northwesterly air advections towards Patagonia (+0.14ºC dec⁻¹ at 850 hPa). Since the 1940s, Patagonian glaciers have lost 1,350 ± 449 Gt of ice, equivalent to 3.7 ± 1.2 mm of global mean sea-level rise.

Reference: Noël, B., Lhermitte, S., Wouters, B. et al. Poleward shift of subtropical highs drives Patagonian glacier mass loss. Nat Commun 16, 3795 (2025). https://doi.org/10.1038/s41467-025-58974-1

The Woods Hole Oceanographic Institution has built the Martha's Vineyard Coastal Observatory (MVCO) near South Beach in Edgartown, Massachusetts. The project was initiated by scientists in the Coastal and Ocean Fluid Dynamics Laboratory (COFDL) at WHOI, who will use the observatory to study coastal atmospheric and oceanic processes.

     Data from the observatory are downloaded from the shore lab every
     twenty minutes: 5, 25 & 45 minutes after the hour. They are processed
     to provide burst averaged statistics, with the most current data
     presented on the MVCO home page. Summary files of the meteorological
     and oceanographic data are provided in the MetDat_s files. Historical
     burst averaged data can be retrieved via a web interface (JGOFS
     format) or anonymous ftp (mvcodata.whoi.edu).

     The time (Yday) is GMT (UTM) at the start of the burst, where 1.5
 represents
     noon on January 1.

     Anonymous ftp ("ftp://mvcodata.whoi.edu/pub/mvcodata/data/") access
 provides
     ascii flat files including year-to-date data in files named
 YYYY_InstID.CNN
     (eg., 2002_Campmt.C03) or data from each day as YYYYyday_InstID.CNN
 (eg.,
     2002122_Campmt.C03). The Yday 1.5 represents noon on January 1. Data are
     available to download. The user must navigate to the desired
 Instrument_ID
     (eg., Campmt_s) and the desired year (eg., 2000). Text files and header
 files
     define the contents of these data files. The year-to-date files can be
 read
     using one of the example Matlab m-files.

     The web-interface ("http://mvcodata.whoi.edu/jg/dir/mvco/") provides
 methods to
     subset the data. Retrieved ascii data will contain a header at the top.
 The
     web-interface also provides interactive graphics (Simple X-Y plotting).

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.

This dataset includes Geotiff. files for the spatial distribution of porosity, 210Pb rain rates and mass accumulation rates in the Skagerrak at a 500m x 500m resolution.

Local weather can influence the growth and development of young birds, either indirectly, by modifying prey availability, or directly, by affecting energetic trade‐offs. Such effects can have lasting implications for life history traits, but the nature of these effects may vary with the developmental stage of the birds, and over timescales from days to weeks. We examined the interactive effects of temperature, rainfall and wind speed on the mass of nestling and fledgling Barn Swallows Hirundo rustica, both on the day of capture and averaging weather across the time since hatching. At the daily timescale, nestling mass was negatively correlated with temperature, but the strength of this association depended on the level of rainfall and wind speed; nestlings were typically heavier on dry or windy days, and the negative effect of temperature was strongest under calm or wet conditions. At the early lifetime timescale (i.e. from hatching to post‐fledging), nestling mass was negatively correlated with temperature at low wind speed. Fledgling body mass was less sensitive to weather; the only weather effects evident were a negative correlation with temperature at the daily scale under high rainfall that became slightly positive under low rainfall. These changes are consistent with weather effects on availability and distribution of insects within the landscape (e.g. causing high concentrations of flying insects), and with the effects of weather variation on nest microclimate. These results together demonstrate the impacts of weather on chick growth, over immediate (daily) and longer term (nestling/fledgling lifetime) timescales. This shows that sensitivity to local weather conditions varies across the early lifetime of young birds (nestling‐fledgling stages) and illustrates the mechanisms by which larger scale (climate) variations influence the body condition of individuals.

Methods For details of colleciton see Facey et al. 2020 https://doi.org/10.1111/ibi.12824

Headings "Female" and "Chick" (here referring to both nestling and fledgling, see under “Groups) refer to identities of individuals derived from ring/band numbers.

Chick = 8-12 days old

Fledgling = 20+ days

Attempt – breeding attempt, second breeding attempt was considered to be any breeding attempt by the same female that followed a successful first breeding attempt.

Brood Size – maximum number of chicks recorded in the nest

Age – hatching to “Day”

Time – hour during which individuals was handled/weighed (24 hour clock)

Day – day of handling/weighed, where day 1 = 1st April

Mass – weighed to the nearest 0.1 g using an electronic balance (Satrue SA-500 http://www.satrue.com.tw/dp2.htm).

Weather data

see Facey et al. 2020 https://doi.org/10.1111/ibi.12824 for details on the origins and handling of weather data.

Temperature (oC) - mean of the daily maximum and daily minimum values

Wind speed (km/h) – daily mean

Rainfall (mm) – total of daily totals.

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 daily rainfall is the most important and demanded input of water resources studies, challenged by typically low density and/or poor quality of in-situ observations. However, the satellite earth observation, through freely available web-based products, can provide complementary rainfall data. Such data is however, typically affected by substantial error, particularly at daily temporal resolution. Therefore, effective methods and protocols of rainfall downscaling, validation, and bias-correction are needed. The aims of this study were to: i) validate two downscaled satellite-derived daily rainfall products, CHIRPS and MPEG, against in-situ observations; ii) merge the downscaled products with in-situ observations to improve their accuracy and evaluate them to select better performing one. This study was conducted at topographically complex, Upper Tekeze Basin (UTB), separately for the wet and dry seasons, within 1 January 2015 – 31 December 2018. Validation of the products, downscaled by nearest-neighbor (NN) and bilinear (BL) methods, was carried out using descriptive statistics, categorical statistics and bias decomposition methods, introducing novel protocol with new bias indicators for each of the evaluation methods. The validation showed large biases of CHIRPS and of MPEG, larger for CHIRPS than for MPEG, larger in dry than in wet season and slightly larger for NN than for BL. To correct biases of the downscaled CHIRPS and MPEG, each was merged with the in-situ observed rainfall applying Geographically Weighted Regression (GWR) algorithm and using rainfall dependence on altitude as explanatory variable. The GWR-merging method substantially improved the accuracy of the MPEG and CHIRPS, with slightly better final accuracy of MPEG than of CHIRPS, better in wet than in dry season. This study confirmed that GWR-merged method could substantially reduce daily bias of satellite rainfall products, even in topographically complex areas, such as the UTB. Further improvement of the method application, can be achieved by densifying rain-gauge network and eventually by adding accuracy-effective explanatory variable(s). Date Submitted: 2021-07-07

Year 2016 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.

The Rain Gauges and Precipitation Sensor market is integral to various industries, providing vital data essential for agriculture, meteorology, environmental monitoring, and urban planning. These instruments help in accurately measuring rainfall and assessing precipitation patterns, allowing for informed decision-ma

This proposal is the exploratory step for Kerguelen component of the GLACIOCLIM Observatory. GLACIOCLIM is a French observatory to globally detect, monitor and understand climate and mass balance variability in the glacial environment. In the Kerguelen archipelago (49�S, 69�E, with an ice covered area of 552km� in 2001), there have been few short term glaciological studies on Ampere Glacier (main glacier of Cook icecap). Paleoclimatic reconstructions over the holocene and long term data from oceanographic and meteorological observatories are also available to get information on the climatic variability during the last 50 years. However, even though these data are essential, a study of the climate-glacier relationship is still necessary to describe the main factors that induced the current dramatic retreat of the Cook icecap. Studying Kerguelen ice caps has become urgent, but is also logistically feasible. The current project plans to deploy and maintain a surface mass balance network, and meteorological instruments on and around the glacier according to GLACIOCLIM protocols. Topographic and hydrological measurements are also planned in order to get data for independent computation of the mass balance. Finally, we will go a step further in the description of the past glacier fluctuation history by dating moraines with a new lichenometric approach.

AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) is developed by organically combining the ERA5-Land dataset with high spatiotemporal resolutions and continuity and the APHRODITE dataset with high quality.

How to cite: Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., Xu, X., 2022. AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1 hourly, 1951–2015, Asia) anchoring the ERA5-Land under the total volume control by APHRODITE. Bulletin of American Meteorological Society, 103 (4)., DOI: https://doi.org/10.1175/BAMS-D-20-0328.1.

Data Format: GeoTIFF

Spatial Coverage: 60°E–150°E, 15°S–55°N, land.

AERA5-Asia (0.1°/ hourly, 1951–1966, Asia) is available at https://doi.org/10.5281/zenodo.6367463

AERA5-Asia (0.1°/ hourly, 1962–1981, Asia) is available at https://doi.org/10.5281/zenodo.6369796

AERA5-Asia (0.1°/ hourly, 1982–1998, Asia) is available at https://doi.org/10.5281/zenodo.4266081

Capturing the spatial and temporal variability of precipitation at fine scale is necessary for high-resolution modeling of snowpack and glacier mass balance in alpine terrain. In this study, we assess the impact of three sub-kilometer precipitation datasets on distributed simulations of snowpack and glacier mass balance with the detailed snowpack model Crocus for winter 2011–2012. The different precipitation datasets at 500-m grid spacing over the northern and central French Alps are coming from (i) the SAFRAN reanalysis specially developed for alpine terrain interpolated at 500-m grid spacing, (ii) the numerical weather prediction (NWP) system AROME at 2.5-km resolution downscaled with a precipitation-elevation adjustment factor, and (iii) a version of AROME at 500-m grid spacing. The spatial patterns of seasonal snowfall are first analyzed for the different precipitation datasets. Large differences between SAFRAN and the two versions of AROME are found at high-altitude and in regions of strong orographic precipitation enhancement. Results of Crocus snowpack simulations are then evaluated against (i) point measurements of snow depth, (ii) maps of snow covered areas retrieved from optical satellite data (MODIS) and (iii) field measurements of winter accumulation of six glaciers. The two versions of AROME lead to an overestimation of snow depth and snow-covered area, which are substantially improved by SAFRAN. However, all the precipitation datasets lead to an underestimation of snow depth increase at the daily scale and cumulated over the season, with AROME 500 m providing the best performances at the seasonal scale. The low correlation found between the biases in snow depth and in cumulated snow depth increase illustrates that total snow depth has a limited significance for the evaluation of precipitation datasets. Measurements of glacier winter mass balance showed a systematic underestimation of high-elevation snow accumulation with SAFRAN. The two versions of AROME overestimate the winter mass balance at four glaciers and produce nearly unbiased estimations for two of them. Our study illustrates the need for improvements in the precipitation field from high-resolution NWP systems for snow and glacier modeling in alpine terrain.