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opencc-zeroDec 2021View details →

Phenotypic variation and genome-wide association studies of main culm panicle node number, maximum node production rate, and degree-days to heading in rice

To understand the genetic basis of main culm panicle node number, maximum node production rate, and degree-days to heading in rice (Oryza sativa), we conducted genome-wide association studies using a diversity panel of 220 rice accessions and 854,832 SNP markers generated using genotyping-by-sequencing (GBS), with 1X coverage. The raw genotype data was filtered, selecting single nucleotide polymorphisms (SNPs) having less than 50% missing data and minimum allele frequency (MAF) >5%. After initial filtering, imputation was conducted using BEAGLE V4.0 in 1,075,302 SNP markers. After imputation, the dataset was filtered a second time by removing SNPs with less than 5% MAF and more than 5% missing data. A total of 854,832 SNPs were used in the genome-wide association analyses. The dataset representing the genotype data of 854,832 SNP markers by 220 rice accessions is presented here.

opencc-zeroJul 2019View details →

Data from: Coffee berry borer (Hypothenemus hampei) (Coleoptera: Curculionidae) development across an elevational gradient on Hawai'i Island: applying laboratory degree-day predictions to natural field populations

Coffee berry borer (CBB, Hypothenemus hampei) (Coleoptera: Curculionidae: Scolytinae) is the most destructive pest of coffee worldwide. Information on CBB development times can be used to predict the initiation of new infestation cycles early in the coffee-growing season and thus inform the timing of insecticide applications. While laboratory estimates of CBB development under constant conditions exist, they have not been applied under the heterogeneous environmental conditions that characterize many coffee-growing regions. We measured CBB development times and abundance in commercial coffee farms across an elevational gradient on Hawai'i Island and applied thermal accumulation models from previous laboratory studies to test their fit to field data. Artificial lures were used to infest coffee berries at five farms ranging in elevation from 279-792 m, and weather variables were monitored at macro (farm-level) and micro (branch-level) scales. CBB development was followed in the field from the time of initial berry infestation by the founding female through the development of F1 mature adults. Mean development time from egg to adult across all sites was 38.5 ± 3.46 days, while the mean time required for the completion of a full life cycle (from time of infestation to presence of mature F1 females) was 50.9 ± 3.35 days. Development time increased with increasing elevation and decreasing temperature. Using macro-scale temperature data and two different estimates for the lower temperature threshold (14.9°C and 13.9°C), we estimated a mean requirement of 332 ± 14 degree-days and 386 ± 16 degree-days, respectively, from the time of berry infestation to the initiation of a new reproductive cycle in mature coffee berries. Similar estimates were obtained using micro-scale temperature data, indicating that macro-scale temperature monitoring is sufficient for life-cycle prediction. We also present a model relating elevation to number of CBB generations per month. Our findings suggest that CBB development times from laboratory studies are generally applicable to field conditions on Hawai'i Island and can be used as a decision support tool to improve IPM strategies for this worldwide pest of coffee.

zenodo32/100

8-day Vegetation optical depth (VOD) and normalised difference vegetation index (NDVI)- based estimated degree of curing (DOC) for Australia

This is a gridded degree of curing (DOC) dataset over Australia based on vegetation optical depth (VOD) and normalised difference vegetation index (NDVI) that can reasonably reproduce groundbased observations in space and time. The gridded DOC data is produced via estimation models using the VOD dataset from AMSR-E (0.1 degree; 8-day) and NDVI dataset from MODIS Terra MOD09A1 (0.005 degree; 8-day). The estimation models are derived from the calibration and evaluation of VOD and NDVI datset with field observed DOC over Australia. Matlab was used for the calibration and evaluation of these models. There are 2 variations based on the following estimation models: DOC_M1 = 145.57-260.82(NDVI)+137.19(VOD)(NDVI) DOC_M2 = 48.70+147.60(VOD)-259.95(VOD)(NDVI) The domain covered is Australia with a 0.05 degree spatial resolution. Temporal resolution is 8-day composites from 04/07/2002 to 26/06/2011 . These experiments were executed by Waisin Chaivaranont of the ARC Centre of Excellence for Climate System Science (ARCCSS) research program "The role of land surface forcing and feedbacks for regional climate".

opencc-by-nc-nd-4.0Dec 2017View details →

publicJul 2019View details →

Data from: Coffee berry borer (Hypothenemus hampei) (Coleoptera: Curculionidae) development across an elevational gradient on Hawai'i Island: applying laboratory degree-day predictions to natural field populations

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publicJan 2022View details →

Phenotypic variation and genome-wide association studies of main culm panicle node number, maximum node production rate, and degree-days to heading in rice

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publicNov 2019View details →

Data from: Does an early spring indicate an early summer? relationships between intra-seasonal growing degree day thresholds

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GPM SSMI on F13 (GPROF) Climate-based Radiometer Precipitation Profiling L3 1 day 0.25 degree x 0.25 degree V07 (GPM_3GPROFF13SSMI_DAY_CLIM) at GES DISC

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. The "CLIM" products differ from their "regular" counterparts (without the "CLIM" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the "CLIM" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.

GPM AMSUB on NOAA17 (GPROF) Climate-based Radiometer Precipitation Profiling L3 1 day 0.25 degree x 0.25 degree V07 (GPM_3GPROFNOAA17AMSUB_DAY_CLIM) at GES DISC

The "CLIM" products differ from their "regular" counterparts (without the "CLIM" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the "CLIM" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.

GPM IMERG Early Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDE) at GES DISC

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.This dataset is the GPM Level 3 IMERG *Early* Daily 10 x 10 km (GPM_3IMERGDE) derived from the half-hourly GPM_3IMERGHHE. The derived result represents an early (expedited) estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before "07", in the simple daily totals for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared (and rain gauge in the final) dataset, variable "precipitation", and appears in higher latitudes. Thus, in most cases users of global "precipitation" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable "MWprecipitation", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version "07", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. The latency of the derived Early daily product is a couple of minutes after the last granule of GPM_3IMERGHHE for the UTC data day is received at GES DISC. Since the target latency of GPM_3IMERGHHE is 4 hours, the daily should appear about 4 hours after the closure of the UTC day. For information on the original data (GPM_3IMERGHHE), please see the Documentation (Related URL). The daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24. Thus, for every grid cell we have Pdaily_mean = SUM{Pi * 1[Pi valid]} / Pdaily_cnt * 24, i=[1,Nf]Where:Pdaily_cnt = SUM{1[Pi valid]}Pi - half-hourly input, in (mm/hr)Nf - Number of half-hourly files per day, Nf=481[.] - Indicator function; 1 when Pi is valid, 0 otherwisePdaily_cnt - Number of valid retrievals in a grid cell per day.Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files.Note that Pi=0 is a valid value.Pdaily_cnt are provided in the data files as variables "precipitation_cnt" and "MWprecipitation_cnt", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. There are various ways the daily error could be estimated from the source half-hourly random error (variable "randomError"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly "randomError" for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):Perr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err * 24}^0.5, i=[1,Nf]Ncnt_err = SUM( 1[Perr_i valid] )where:Perr_i - half-hourly input, "randomError", (mm/hr)Perr_daily - Magnitude of the daily error, (mm/day)Ncnt_err - Number of valid half-hour e

GPM DPR Precipitation Profile 1 Day Ascending 0.25 degree x 0.25 degree V07 (GPM_3DPR_ASC) at GES DISC

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.. The Level 3 DPR products present the user with summary information over daily and monthly time periods. These gridded products are in a convenient gridded form and can be used easily in comparisons with other satellite and ground data. The Level 3 DPR algorithm accumulates instantaneous precipitation estimates from the Level 2 retrieval algorithms into grids over a day and month time span. There are two grid resolutions: 5.0 degrees and 25 kms. For each grid box, the core statistics are the number of measurements, mean, and standard deviation. Most variables are also conditioned on surface type and precipitation type with other three-dimensional fields adding the height above the ellipsoid. Unless otherwise specified, the means are conditioned on precipitation being present (rain rate > 0). For the daily product, the mean square statistic is saved rather than the standard deviation. In addition to the daily and monthly products is a simplified joint daily product that contains a subset of the fields from the full daily product.

GPM GMI (GPROF) Climate-based Radiometer Precipitation Profiling L3 1 day 0.25 degree x 0.25 degree V07 (GPM_3GPROFGPMGMI_DAY_CLIM) at GES DISC

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The "CLIM" products differ from their "regular" counterparts (without the "CLIM" in the name) by the ancillary data they use. They are Climate-Reference products, which requires homogeneous ancillary data over the climate time series. Hence, the ECMWF-Interim (European Centre for Medium-Range Weather Forecasts, 2-3 months lag behind the regular production) reanalysis is used as ancillary data to derive surface and atmospheric conditions required by the GPROF algorithm for the "CLIM" output. The GPROF databases are also adjusted accordingly for these climate-referenced retrievals.3GPROF products provide global gridded monthly/daily precipitation averages from multiple satellites that can be used for climate studies. The 3GPROF products are based on retrievals from high-quality microwave sensors, which are sensitive to liquid and ice-phase precipitation hydrometeors in the atmosphere.

GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDF) at GES DISC

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.This dataset is the GPM Level 3 IMERG *Final* Daily 10 x 10 km (GPM_3IMERGDF) derived from the half-hourly GPM_3IMERGHH. The derived result represents the Final estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before "07", in the simple daily totals for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared and rain gauge dataset, variable "precipitation", and appears in higher latitudes. Thus, in most cases users of global "precipitation" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable "MWprecipitation", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version "07", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. The latency of the derived *Final* Daily product depends on the delivery of the IMERG *Final* Half-Hourly product GPM_IMERGHH. Since the latter are delivered in a batch, once per month for the entire month, with up to 4 months latency, so will be the latency for the Final Daily, plus about 24 hours. Thus, e.g. the Dailies for January can be expected to appear no earlier than April 2. The daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24. Thus, for every grid cell we have Pdaily_mean = SUM{Pi * 1[Pi valid]} / Pdaily_cnt * 24, i=[1,Nf]Where:Pdaily_cnt = SUM{1[Pi valid]}Pi - half-hourly input, in (mm/hr)Nf - Number of half-hourly files per day, Nf=481[.] - Indicator function; 1 when Pi is valid, 0 otherwisePdaily_cnt - Number of valid retrievals in a grid cell per day.Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files.Note that Pi=0 is a valid value.Pdaily_cnt are provided in the data files as variables "precipitation_cnt" and "MWprecipitation_cnt", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. There are various ways the daily error could be estimated from the source half-hourly random error (variable "randomError"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly "randomError" for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):Perr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err * 24}^0.5, i=[1,Nf]Ncnt_err = SUM( 1[Perr_i valid] )where:Perr_i - half-hourly input, "randomError", (mm/hr)Perr_daily - Magnitude of the daily error, (mm/day)Ncnt_err - Number of valid half-hour error estimatesAga

GPM DPR Spectral Latent Heating Profiles L3 1 day 0.5 degree x 0.5 degree V07 (GPM_3HSLH_DAY) at GES DISC

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The Gridded Spectral Latent Heating (3HSLH) products contain latent heating, Q1-QR and Q2 profiles from DPR raindata.

OMI/Aura Multi-wavelength Aerosol Optical Depth and Single Scattering Albedo L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3 (OMAEROe) at GES DISC

The OMI science team produces this Level-3 Aura/OMI Global Aerosol Data Products OMAEROe (0.25deg Lat/Lon grids). The OMAEROe product selects best aerosol value from the Level2G good quality data that are reported in each grid, based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. The selection criteria is based on the shortest optical path length (secant of solar zenith angle + secant of viewing zenith angle).The OMAEROe files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAEROe data product is about 7 Mbytes. (The shortname for this Level-3 Global Gridded Aerosol Product is OMAEROe)