Gridded Weather Data Research Articles

Empirical prediction of snow and land-fast sea ice temperature from surface meteorology on Pistolet Bay, Northern Newfoundland

Abstract The temperature of sea ice and snow cover on the ice surface is a critical component of thermodynamic models of sea ice. Ice temperature is also an important factor in the sea ice strength and therefore in the potential loads the ice can exert on ships, ports, bridges, and offshore structures. The snow layer, while remaining distinct from the sea ice in its physical properties, can become compacted on the ice and contribute to the effective ice thickness and strength. The temperature of sea ice is governed by the atmospheric energy flux balance at the snow surface and the ocean energy flux at the ice base. In addition, snow cover acts as an insulator to sea ice, buffering it against temperature changes due to changes in surface meteorological conditions. The average internal ice temperature and total thickness (through its impact on ice salinity and brine volume) significantly affect ice strength parameters used in the calculation of loads on engineered structures. In February 2017–2019, three field campaigns were carried out on the land-fast ice on Pistolet Bay, north of St. Anthony, Newfoundland in Atlantic Canada. For approximately five days in 2017 and about four days in 2018–2019, surface air temperatures, wind speeds, and snow and ice temperature profiles were continuously measured. Dew point temperatures were obtained from the North American Regional Reanalysis (NARR) dataset. The datasets from the three field campaigns were used to develop empirical linear regression models of snow and ice surface and depth-mean temperatures as a function of the air temperature alone, and of the air and dew point temperatures and wind speed. In this paper, the meteorological data from the three field campaigns are presented, along with the snow and ice thickness and temperature data. The linear models of snow and ice temperature are presented, tested using the observed field data from the Pistolet Bay campaigns, and compared with results obtained from previously published models of snow and sea ice temperature. The results show that the models of snow and ice temperature as a function of the three meteorological variables are modestly more robust than the models as a function of air temperature alone, with R2 values of up to 0.90. The models presented in this paper have potential usefulness in improved prediction of site-specific snow/ice temperatures and associated temperature-dependent ice properties, such as strength at sites where gridded weather data or in-situ meteorological measurements are available, but in-situ snow/ice temperature measurements are nonexistent.

Characterizing Brazilian soybean-growing regions by water deficit patterns

The use of specific crop management in a growing region according to its climate can reduce the yield gap caused by water deficit, the main cause of soybean yield gap in Brazil. Therefore, this study aimed to characterize the Brazilian soybean-growing regions based on attainable yield and water deficit patterns. The attainable yields were estimated by the crop model DSSAT-CROPGRO-Soybean, considering six sowing dates, three soil types and 33 growing seasons (1980–2013), using gridded weather data with 453 locations. The homogenous regions were defined by the cluster analysis, based on attainable yield. The water deficit pattern (WDP) was identified by a simulated 5-day running average of water stress index (WSPD) during the growing seasons across different planting dates and soil types, using the cluster analysis for each homogenous region. The attainable yield varied considerably between environments, from 0 to 3400 kg ha−1. Ten environmental zones were obtained, where locations in the same zone indicated the same level and variability of the attainable yield across sowing dates, soils and growing seasons. The highest attainable yield (> 2800 kg ha−1) occurred in the central-western to northern regions (zones 1, 2 and 3), covering most soybean growing areas in Brazil. The lowest attainable yield was observed in the extreme South (zone 6) and East border (zone 7) (< 1170 kg ha−1). The highest attainable yield variability occurred in northern/northeastern (zones 4 and 5), eastern (zone 8) and southern (zones 9 and 10) regions. Three WDP were obtained in each environmental zone, which differed in frequency, level and moment of occurrence during the soybean growing season. Sowing dates and soil types contributed to defining the frequency of each WDP. The homogenous zones and WDP demonstrate that attainable yield and yield gap by water deficit can differ significantly across Brazilian soybean-growing regions. These patterns could be used to improve crop management, such as sowing dates, preferential soils, and irrigation requirements, as well as breeding strategies to deal with water deficit, namely deeper root systems, stomatal closure by decreased water content in the soil and drought tolerance of nitrogen fixation.

A case study for the assessment of the suitability of gridded reanalysis weather data for hydrological simulation in Beas river basin of North Western Himalaya

The major problem of estimating snowmelt runoff for Beas river basin is inadequacy of observed meteorological data distributed across the basin. In this study, ERA-Interim global reanalysis data have been used for assessing the stream flow and sediment yield in Beas river basin of North Western Himalaya. The snow module of ARCSWAT hydrology model has been simulated by integration of subbasin-wise elevation band files for modeling snowmelt runoff process including sediment yield due to rainfall and temperature change for different elevation bands varying from 361 to 6188 m. The gridded reanalysis (0.125° × 0.125°) dataset produces a decreased maximum and minimum temperature and increased precipitation at higher elevation in comparison with IMD gridded weather data. The outcome of this study conveys that the reanalysis data represent better snowmelt runoff (NSE = 0.76, 0.70 and R2 = 0.80, 0.70) and sediment yield (NSE = 0.50, 0.53 and R2 = 0.72, 0.57) mechanism at Pong and Pandoh dams than IMD gridded weather data (NSE = 0.50, 0.47 and R2 = 0.65, 0.60) for stream flow and (NSE = 0.50, 0.53 and R2 = 0.65, 0.60) sediment yield during the period 1996–1999 and 1999–2002 for these two locations.

Evaluation of the Hargreaves-Samani Method for Estimating Reference Evapotranspiration with Ground and Gridded Weather Data Sources

Abstract. Methods of estimating evapotranspiration require weather variables as their main input data. Thus, the lack of full weather data sets is one of the main challenges for evaluating and mitigating the effects of climate variability and climate change on agricultural production systems. The Hargreaves-Samani method (HS) is one of the ways to estimate reference evapotranspiration (ETo) when only temperature observations are available, which is a common situation in many agricultural enterprises. Another possible option for regions not served by weather stations is the use of gridded weather data (GWD). Based on that, the main objective of this study was to evaluate the performance of the HS method to estimate ETo in different regions of the United States, as well as to assess the suitability of two gridded weather data (PRISM and NOAA-RTMA) sources to estimate ETo by comparing the results obtained to ETo estimated by the Penman-Monteith (FAO-PM) method, which is the recommended methodology by FAO Irrigation and Drainage Paper 56 when all weather variables are available. Weather observations were obtained for 17 locations across the United States representing regions with subtropical humid and semi-arid continental climates, considering the period of one year (2017). These observations were used to estimate daily ETo with the HS and Penman-Monteith methods. Our results demonstrated that the HS method performance varied according to the location and month of the year. Due to the high relative humidity (RH) during the winter, and high air temperature (Ta) during the summer, the locations selected in the state of Florida, presented the worst performance. The HS method performed well in many other locations such as Froid – MT. Also, the estimation of ETo by HS method and by using PRISM and NOAA-RTMA gridded weather databases showed a good agreement with the ETo estimated by FAO-PM based on weather station observations. Keywords: Penman-Monteith, PRISM, RTMA, Water Management.

Estimation of Coffee Yield from Gridded Weather Data

Core Ideas Use of gridded weather data [European Center for Medium‐Range Weather Forecasts (ECMWF) and NASA] gave similar estimations of coffee yield to the models that use surface‐observed weather data. A new calibration was proposed to the model to the penalties and the coefficients calibrated from the different data sources. The calibration of the coefficients for the ECMWF and NASA data was efficient. The tests with independent data confirmed the improvement of the calibration. Coffee yield can be estimated using the ECMWF and NASA grid data. Agrometeorological models have been applied to crop yield estimations, providing information for management. A limiting factor of these models is the input data, which come mostly from surface meteorological stations (SMS). We propose the use of gridded weather data provided by the European Center for Medium‐Range Weather Forecasts (ECMWF) and the NASA as a viable and innovative alternative for coffee (Coffea arabica L.) yield estimation in Brazil. We made modifications in the coefficients of the model proposed by Santos and Camargo, regarding the penalties for extreme temperatures and those related to different gridded data sources. The accuracy, measured by the mean absolute percentage error (MAPE), was 23.76, 24.61, and 22% for the calibrations with ECMWF, NASA, and SMS data, respectively. These high levels of MAPE were the result of the high biennality of the crop. The average tendency, measured by the systematic root mean square error, was an overestimation (or underestimation) of ±465 kg ha−1 by the ECMWF, ±411 kg ha−1 by NASA, and ±653 by the SMS models. The mean precision, measured by the nonsystematic root mean square error, was 186, 190, and 280 kg ha−1 for the ECMWF, NASA, and SMS models, respectively. These results indicate that coffee yield for São Paulo and Minas Gerais can be calibrated and estimated with the ECMWF and NASA gridded data.

Solar Radiation Models and Gridded Databases to Fill Gaps in Weather Series and to Project Climate Change in Brazil

The quantification of climate change impacts on several human activities depends on reliable weather data series, without gaps and long enough to build up future climate. Based on that, this study aimed to evaluate the performance of temperature-based models for estimating global solar radiation and gridded databases (AgCFSR, AgMERRA, NASA/POWER, and XAVIER) as alternative ways for filling gaps in historical weather series (1980–2009) in Brazil and to project climate change scenarios based on measured and gridded weather data. Projections for mid- and end-of-century periods (2040–2069 and 2070–2099), using seven global climate models from CMIP5 under intermediate (RCP4.5) and high (RCP8.5) emission scenarios, were performed. The Bristow–Campbell model was the one that best estimated solar radiation, whereas the XAVIER gridded database was the closest to observed weather data. Future climate projections, under RCP4.5 and RCP8.5 scenarios, as expected, showed warmer conditions for all scenarios over Brazil. On the contrary, rainfall projections are more uncertain. Despite that, the rainfall amounts will be reduced in the North-Northeast region and increased in Southern Brazil. No significant differences between projections using the observed and XAVIER gridded database were observed; therefore, such a database showed to be reliable for both to fill gaps and to generate climate change scenarios.

Distributed Control of High-Altitude Balloon Formation by Extremum-Seeking Control

High-altitude balloons are a promising technology for providing high-speed wireless Internet to the entire Earth. These balloons float passively on wind currents in the stratosphere and connect to nearby Internet users on Earth. To connect to everyone on Earth, a fleet of several thousands balloons must be arranged in an evenly spread-out formation, so that every user is close to at least one balloon. Since the balloons naturally move with the wind, an important problem is to control the formation of the fleet of balloons. In this paper, we show that distributed extremum-seeking control (ESC) can be used to control the balloon formation. This approach is based on the minimization of a cost function and does not require a model of wind currents or Internet users. We provide several cost functions, which can be used by the ESC and are based on the distance between balloons, Voronoi partitions, a variant of Lloyd’s algorithm, Internet bandwidth, and the number of Internet users. As the control architecture is fully distributed, the computational burden is shared between all the balloons, and the algorithm can be scaled to an arbitrarily large network of balloons. Several simulations involving 1200 balloons are used to verify the effectiveness of this approach. The simulations use realistic nonlinear wind models, which are obtained by interpolating gridded weather data obtained from the National Oceanic and Atmospheric Administration.

Can next-generation soil data products improve soil moisture modelling at the continental scale? An assessment using a new microclimate package for the R programming environment

Accurate models of soil moisture are vital for solving core problems in meteorology, hydrology, agriculture and ecology. The capacity for soil moisture modelling is growing rapidly with the development of high-resolution, continent-scale gridded weather and soil data together with advances in modelling methods. In particular, the GlobalSoilMap.net initiative represents next-generation, depth-specific gridded soil products that may substantially increase soil moisture modelling capacity. Here we present an implementation of Campbell’s infiltration and redistribution model within the NicheMapR microclimate modelling package for the R environment, and use it to assess the predictive power provided by the GlobalSoilMap.net product Soil and Landscape Grid of Australia (SLGA, ∼100 m) as well as the coarser resolution global product SoilGrids (SG, ∼250 m). Predictions were tested in detail against 3 years of root-zone (3–75 cm) soil moisture observation data from 35 monitoring sites within the OzNet project in Australia, with additional tests of the finalised modelling approach against cosmic-ray neutron (CosmOz, 0–50 cm, 9 sites from 2011 to 2017) and satellite (ASCAT, 0–2 cm, continent-wide from 2007 to 2009) observations. The model was forced by daily 0.05° (∼5 km) gridded meteorological data. The NicheMapR system predicted soil moisture to within experimental error for all data sets. Using the SLGA or the SG soil database, the OzNet soil moisture could be predicted with a root mean square error (rmse) of ∼0.075 m3 m−3 and a correlation coefficient (r) of 0.65 consistently through the soil profile without any parameter tuning. Soil moisture predictions based on the SLGA and SG datasets were ≈ 17% closer to the observations than when using a chloropleth-derived soil data set (Digital Atlas of Australian Soils), with the greatest improvements occurring for deeper layers. The CosmOz observations were predicted with similar accuracy (r = 0.76 and rmse of ∼0.085 m3 m−3). Comparisons at the continental scale to 0–2 cm satellite data (ASCAT) showed that the SLGA/SG datasets increased model fit over simulations using the DAAS soil properties (r ∼ 0.63 &rmse 15% vs. r 0.48 &rmse 18%, respectively). Overall, our results demonstrate the advantages of using GlobalSoilMap.net products in combination with gridded weather data for modelling soil moisture at fine spatial and temporal resolution at the continental scale.

Vertical Gravimeter Array Observations and Their Performance in Groundwater‐Level Monitoring

AbstractThe gravitational effects of the atmosphere and subsurface water are significant obstacles to observing gravity variations on the sub‐μGal (1 μGal = 10 nm/s2) scale. The goal of this study is to detect changes in gravity that are caused by mass redistributions deep underground related to seismological phenomena by reducing environmental gravity effects using multiple gravimeters belowground and aboveground, which we term a “vertical gravimeter array.” Based on an evaluation of the responses to atmospheric effects and rainfall events identified in observations made with individual relative gravimeters, the vertical gravimeter array succeeds in stacking the target signals from deep underground and in reducing errors due to rainfall or free groundwater and atmospheric effects. To enable accurate interpretation, we introduce a physical approach that is based on attraction and loading deformation effects for atmospheric reduction using state‐of‐the‐art gridded weather data products. Changes in the water levels of confined groundwater can be regarded as a signal from deep underground, and a response coefficient of approximately −15 μGal/m was obtained. In addition, the response coefficient of the free groundwater level was determined to be approximately 5 μGal/m. Such array observations are expected to contribute to monitoring crustal activity and hydrological studies.

Evaluation of ridge regression for country-wide prediction of genotype-specific grain yields of wheat

Large scale prediction of the performance of genotypes is fundamental for understanding genotype by environmental interactions (G × E), predicting accurately genotypic performance in specific environments, and increasing our knowledge to develop future crop varieties.We derived environmental limiting factors from daily weather data for critical crop growth phases of wheat (Triticum aestivum), using a winter wheat suitability model. The limiting factors that account for the effects of environmental variables on wheat productivity and phenology were then integrated into a matrix of environmental factors that related environments with observations through a ridge regression-best linear unbiased prediction (RR-BLUP) model. Prediction accuracy following a leave-one-site-out validation scheme was evaluated through correlations between predicted and observed yield for six winter wheat genotypes grown at 8 to 10 sites during three years. Accuracy (r = 0.01–0.75) was within the range of values reported in other studies. High prediction accuracies for certain sites and genotypes showed that the use of environmental limiting factors derived from gridded weather data into a RR-BLUP framework is a promising approach to predict genotypic performance in large areas. In contrast, the environmental and crop data collected in the variety trials and how these trials covered the territory limited the accuracy of predictions.

Assessment of different gridded weather data for soybean yield simulations in Brazil

A high-density, well-distributed, and consistent historical weather data series is of major importance for agricultural planning and climatic risk evaluation. A possible option for regions where weather station network is irregular is the use of gridded weather data (GWD), which can be downloaded online from different sources. Based on that, the aim of this study was to assess the suitability of two GWD, AgMERRA and XAVIER, by comparing them with measured weather data (MWD) for estimating soybean yield in Brazil. The GWD and MWD were obtained for 24 locations across Brazil, considering the period between 1980 and 2010. These data were used to estimate soybean yield with DSSAT-CROPGRO-Soybean model. The comparison of MWD with GWD resulted in a good agreement between climate variables, except for solar radiation. The crop simulations with GWD and MWD resulted in a good agreement for vegetative and reproductive phases. Soybean potential yield (Yp) simulated with AgMERRA and XAVIER had a high correlation (r > 0.88) when compared to the estimates with MWD, with the RMSE of about 400 kg ha−1. For attainable yield (Ya), estimates with XAVIER resulted in a RMSE of 700 kg ha−1 against 864 kg ha−1 from AgMERRA, both compared to the simulations using MWD. Even with these differences in Ya simulations, both GWD can be considered suitable for simulating soybean growth, development, and yield in Brazil; however, with XAVIER GWD presenting a better performance for weather and crop variables assessed.

Weather-Based Models for Assessing the Risk of Sclerotinia sclerotiorum Apothecial Presence in Soybean (Glycine max) Fields.

Sclerotinia stem rot (SSR) epidemics in soybean, caused by Sclerotinia sclerotiorum, are currently responsible for annual yield reductions in the United States of up to 1 million metric tons. In-season disease management is largely dependent on chemical control but its efficiency and cost-effectiveness depends on both the chemistry used and the risk of apothecia formation, germination, and further dispersal of ascospores during susceptible soybean growth stages. Hence, accurate prediction of the S. sclerotiorum apothecial risk during the soybean flowering period could enable farmers to improve in-season SSR management. From 2014 to 2016, apothecial presence or absence was monitored in three irrigated (n = 1,505 plot-level observations) and six nonirrigated (n = 2,361 plot-level observations) field trials located in Iowa (n = 156), Michigan (n = 1,400), and Wisconsin (n = 2,310), for a total of 3,866 plot-level observations. Hourly air temperature, relative humidity, dew point, wind speed, leaf wetness, and rainfall were also monitored continuously, throughout the season, at each location using high-resolution gridded weather data. Logistic regression models were developed for irrigated and nonirrigated conditions using apothecial presence as a binary response variable. Agronomic variables (row width) and weather-related variables (defined as 30-day moving averages, prior to apothecial presence) were tested for their predictive ability. In irrigated soybean fields, apothecial presence was best explained by row width (r = -0.41, P < 0.0001), 30-day moving averages of daily maximum air temperature (r = 0.27, P < 0.0001), and daily maximum relative humidity (r = 0.16, P < 0.05). In nonirrigated fields, apothecial presence was best explained by using moving averages of daily maximum air temperature (r = -0.30, P < 0.0001) and wind speed (r = -0.27, P < 0.0001). These models correctly predicted (overall accuracy of 67 to 70%) apothecial presence during the soybean flowering period for four independent datasets (n = 1,102 plot-level observations or 30 daily mean observations).

Improving the accuracy of satellite-based high-resolution yield estimation: A test of multiple scalable approaches

Fast, accurate and inexpensive estimates of crop yields at the field scale are useful for many applications. Based on the Google Earth Engine (GEE) platform, we recently developed a Scalable satellite-based Crop Yield Mapper (SCYM) that integrates crop simulations with satellite imagery and gridded weather data to generate 30m resolution yield estimates for multiple crops in different regions. Existing versions of SCYM typically capture one-third to half of the variation in reported county-scale yields. Using rainfed maize in the US Midwest as an example, this study tested multiple approaches for improving SCYM’s accuracy, including (i) calibrating the phenology parameters of the crop model (APSIM) used to generate training samples for SCYM; (ii) using an ensemble of three crop models (APSIM-Maize, CERES-Maize, and Hybrid-Maize) instead of a single model; (iii) using simulated biomass from the crop models instead of simulated yields to train SCYM, with the former assuming a constant harvest index (HI). Results show substantial improvement in performance, as assessed using reported county yields by USDA-NASS, both from calibrating APSIM phenology parameters and from training SCYM on simulated biomass rather than yields. Using a multi-model ensemble further improves SCYM, although the benefit is limited. The proposed preferred version of SCYM on average captures 75% of the yield variation for 2001–2015 in the 3I states (i.e. Illinois, Indiana and Iowa) where SCYM is trained, with RMSE typically less than 1t/ha, and explains 41% to 83% of multi-year yield variations when tested across nine Midwestern US states for 2008–2015. This level of accuracy is particularly notable given that only data from 2014 were used to calibrate phenology parameters. The yield estimates for multiple years in multiple states utilized 1184 Landsat tiles, but could be completed in about 2h per year by using the GEE platform. All approaches tested in this study do not require any site-specific measurements, and thus can be readily extended to other regions and crops.

Vulnerability of dairy based livelihoods to climate variability and change-A study of Western Ghat Region-Ratnagiri, Maharashtra, India

This study examines vulnerability of dairy based livelihoods to climate change applying index based approach underlying the principles of Intergovernmental Panel on Climate Change (IPCC), using 28 indicators to construct an overall Livelihood Vulnerability Index (LVI) to climate change, an important step in evolving appropriate adaptation strategies to climate change. Detailed house hold level data of dairy based livelihoods of Ratnagiri region of Western Ghats were collected through Participatory Rural Appraisal (PRA) and personal interviews from 180 randomly selected farmers of three talukas of Ratnagiri district complemented by thirty years of gridded weather data and other secondary data sources. The analysis revealed that the dairy farmers of Lanja taluka were most vulnerable among those of Ratnagiri district. The index score of dairy based livelihoods of Lanja taluka was negative with overall value-4.17 with 48% farmers having high level of vulnerability.

Vulnerability of dairy based livelihoods to climate variability and change- A study of Western Ghat Region- Ratnagiri, Maharashtra, India

This study examines vulnerability of dairy based livelihoods to climate change applying index based approach underlying the principles of Intergovernmental Panel on Climate Change (IPCC), using 28 indicators to construct an overall Livelihood Vulnerability Index (LVI) to climate change, an important step in evolving appropriate adaptation strategies to climate change. Detailed house hold level data of dairy based livelihoods of Ratnagiri region of Western Ghats were collected through Participatory Rural Appraisal (PRA) and personal interviews from 180 randomly selected farmers of three talukas of Ratnagiri district complemented by thirty years of gridded weather data and other secondary data sources. The analysis revealed that the dairy farmers of Lanja taluka were most vulnerable among those of Ratnagiri district.The index score of dairy based livelihoods of Lanja taluka was negative with overall value -4.17 with 48% farmers having high level of vulnerability.

Vulnerability of Dairy-Based Livelihoods to Climate Variability and Change:A Study of Western Ghats Region in Wayanad, Kerala

The study assesses the livelihood vulnerability of dairy farmers to climate variability and change (CVC) in Wayanad district of the Western Ghats region in Kerala. For this purpose, a Livelihood Vulnerability Index (LVI) was developed underlying the definition of Intergovernmental Panel on Climate Change consisting of 28 indicators and 7 LVI components. A fussel framework was used for conceptualizing the vulnerable situation. Participatory rural appraisal and personal interviews were used to collect household data of 180 dairy farmers of three taluks complemented by thirty years of gridded weather data. The normalized data were then combined into three indices, i.e. sensitivity, exposure and adaptive capacity, which were then averaged with weights given using principal component analysis, to obtain the overall index. LVI indicated that the dairy farmers of all the taluks of Wayanad are vulnerable to CVC with Pulpally taluka being the most vulnerable with 48.33% farmers under the high level vulnerability category with wide variation in LVI components across the taluks. For the sustenance of dairy farming of small and marginal farmers of the region and for mitigating risks, policies are required for incentivizing the livelihood infrastructure and promotion of grass root level innovations.

Testing the SWAT Model with Gridded Weather Data of Different Spatial Resolutions

This study explored the influence of the spatial resolution of a gridded weather dataset when inputted in the soil and water assessment tool (SWAT) over the Garonne River watershed. Several datasets are compared: ground-based weather stations, the 8-km SAFRAN product (Système d’Analyse Fournissant des Renseignements Adaptés à la Nivologie), the 0.5° CFSR product (Climate Forecasting System Reanalysis) and several derived SAFRAN grids upscaled to 16, 32, 64 and 128 km. The SWAT model, calibrated on weather stations, was successively run with each gridded weather dataset. Performances with SAFRAN up to 64 or 128 km were poor, due to a contraction of the spatial variance of daily precipitation. Performances with 8-km SAFRAN are similar to that of the aggregated 16- and 32-km SAFRAN grids. The ~30-km CFSR product was found to perform well at some sites, while in others, its performance was considerably inferior because of grid points where precipitation was overestimated. The same problem was found in the calibration, where data at some weather stations did not appear to be representative of the subwatershed in which they are used to compute hydrology. These results suggest that the difference in the representation of the climate was more influential than its spatial resolution, an analysis that was confirmed by similar performances obtained with the SWAT model calibrated on the 16- and 32-km SAFRAN grids. However, the better performances obtained from these two weather datasets than from the ground-based stations’ dataset confirmed the advantage of using the SAFRAN product in SWAT modelling.

From grid to field: Assessing quality of gridded weather data for agricultural applications

Abstract High quality measured weather data (MWD) are not available in many agricultural regions across the globe. As a result, many studies that dealt with global climate change, land use, and food security scenarios and emerging agricultural decision support tools have relied on gridded weather data (GWD) to estimate crop phenology and crop yields. An issue is the agreement of GWD with MWD and the degree to which this agreement may influence the utility of GWD for agricultural research. The objectives of this study were: (i) to compare the agreement of two widely used gridded weather databases (GWDs) (Daymet and PRISM) and MWD, (ii) to evaluate their robustness at simulating maize growth and development, and (iii) to examine how GWD compare relative to weather data interpolated from existing meteorological stations for which MWD are available. The U.S. Corn Belt, a region that accounts for 43 and 34% of respective global maize and soybean production, was used as a case of study because of its dense weather station network and high-quality MWD. Historical daily MWD were retrieved from 45 locations across the region, resulting in ca . 1300 site-years. To test the accuracy of GWDs, separate simulations of maize yield and development were performed, separately for the two GWDs and MWD, using a well-validated maize crop model. For both GWDs, small biases were observed for temperature and growing degree-days in relation with MWD. However, accuracy was much lower for relative humidity, precipitation, reference evapotranspiration, and degree of seasonal water deficit. There was close agreement in duration of vegetative and reproductive phases between GWD and MWD, with root mean square error (RMSE) ranging from 3 to 7 days for the different crop phases and GWDs. However, robustness of GWDs to reproduce maize yields simulated using MWD was lower as indicated by the RMSE (18 and 24% of average yield for Daymet and PRISM, respectively). There was also a high proportion of site-years (20 and 32% for Daymet and PRISM, respectively) exhibiting a yield deviation >15% in relation to the yield simulated using MWD. Data interpolation using a dense weather station network resulted in lower RMSE% for simulated phenology and yields relative to GWDs. Findings from this study indicate that GWD cannot replace MWD as a basis for field-scale agricultural applications. While GWD appear to be robust for applications that only require temperature for prediction of crop stages, GWD should not be used for applications that depend on accurate estimation of crop water balance, crop growth, and yield. We propose that the evaluation performed in this study should be taken as a routinary activity for any research or agricultural decision tool that relies on GWD.

Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications

We developed a mechanism for seamlessly providing weather data and long-term historical climate data from a gridded data source through an international standard web API, which was the Sensor Observation Service (SOS) defined by the Open Geospatial Consortium (OGC). The National Agriculture and Food Research Organization (NARO) Japan has been providing gridded climate data consisting of nine daily meteorological variables, which are average, minimum, maximum of air temperature, relative humidity, sunshine duration, solar radiant exposure, downward longwave radiation, precipitation and wind speed for 35 years covering Japan. The gridded data structure is quite useful for spatial analysis, such as developing crop suitability maps and monitoring regional crop development. Individual farmers, however, make decisions using historical climate information and forecasts for an incoming cropping season of their farms. In this regard, climate data at a point-based structure are convenient for application development to support farmers’ decisions. Through the proposed mechanism in this paper, the agricultural applications and analysis can request point-based climate data from a gridded data source through the standard API with no need to deal with the complicated hierarchical data structure of the gridded climate data source. Clients can easily obtain data and metadata by only accessing the service endpoint. The mechanism also provides several web bindings and data encodings for the clients’ convenience. Caching, including the pre-caching mechanism, was developed and evaluated to secure an effective response time. The mechanism enhances the accessibility and usability of the gridded weather data source, as well as SOS API for agricultural applications.

Improving iterative surface energy balance convergence for remote sensing based flux calculation

A modification of the iterative procedure of the surface energy balance was purposed to expedite the convergence of Monin–Obukhov stability correction utilized by the remote sensing based flux calculation. This was demonstrated using ground-based weather stations as well as the gridded weather data (North American Regional Reanalysis) and remote sensing based (Landsat 5, 7) images. The study was conducted for different land-use classes in southern Idaho and northern California for multiple satellite overpasses. The convergence behavior of a selected Landsat pixel as well as all of the Landsat pixels within the area of interest was analyzed. Modified version needed multiple times less iteration compared to the current iterative technique. At the time of low wind speed (∼1.3 m/s), the current iterative technique was not able to find a solution of surface energy balance for all of the Landsat pixels, while the modified version was able to achieve it in a few iterations. The study will facilitate many operational evapotranspiration models to avoid the nonconvergence in low wind speeds, which helps to increase the accuracy of flux calculations.