Dealing with spatial heterogeneity in pointwise-to-gridded- data comparisons

Most studies on validation of satellite trace gas retrievals or atmospheric chemical transport models assume that pointwise measurements, which roughly represent the element of space, should compare well with satellite (model) pixels (grid box). This assumption implies that the field of interest must possess a high degree of spatial homogeneity within the pixels (grid box), which may not hold true for species with short atmospheric lifetimes or in the proximity of plumes. Results of this assumption often lead to a perception of a nonphysical discrepancy between data, resulting from different spatial scales, potentially making the comparisons prone to overinterpretation. Semivariogram is a mathematical expression of spatial variability in discrete data. Modeling the semivariogram behavior permits carrying out spatial optimal linear prediction of a random process field using kriging. Kriging can extract the spatial information (variance) pertaining to a specific scale, which in turn translates pointwise data to a gridded space with quantified uncertainty such that a grid-to-grid comparison can be made. Here, using both theoretical and real-world experiments, we demonstrate that this classical geostatistical approach can be well adapted to solving problems in evaluating model-predicted or satellite-derived atmospheric trace gases. This study suggests that satellite validation procedures using the present method must take kriging variance and satellite spatial response functions into account. We present the comparison of Ozone Monitoring Instrument (OMI) tropospheric NO2 columns against 11 Pandora spectrometer instrument (PSI) systems during the DISCOVER-AQ campaign over Houston. The least-squares fit to the paired data shows a low slope (OMI=0.76×PSI+1.18×1015 molecules cm−2, r2=0.66), which is indicative of varying biases in OMI. This perceived slope, induced by the problem of spatial scale, disappears in the comparison of the convolved kriged PSI and OMI (0.96×PSI+0.66×1015 molecules cm−2, r2=0.72), illustrating that OMI possibly has a constant systematic bias over the area. To avoid gross errors in comparisons made between gridded data vs. pointwise measurements, we argue that the concept of semivariogram (or spatial autocorrelation) should be taken into consideration, particularly if the field exhibits a strong degree of spatial heterogeneity at the scale of satellite and/or model footprints.

Development of a new high spatial resolution (0.25° × 0.25°) long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region

ABSTRACT. The study discusses development of a new daily gridded rainfall data set (IMD4) at a high spatial resolution (0.25° × 0.25°, latitude × longitude) covering a longer period of 110 years (1901-2010) over the Indian main land. A comparison of IMD4 with 4 other existing daily gridded rainfall data sets of different spatial resolutions and time periods has also been discussed. For preparing the new gridded data, daily rainfall records from 6955 rain gauge stations in India were used, highest number of stations used by any studies so far for such a purpose. The gridded data set was developed after making quality control of basic rain-gauge stations. The comparison of IMD4 with other data sets suggested that the climatological and variability features of rainfall over India derived from IMD4 were comparable with the existing gridded daily rainfall data sets. In addition, the spatial rainfall distribution like heavy rainfall areas in the orographic regions of the west coast and over northeast, low rainfall in the lee ward side of the Western Ghats etc. were more realistic and better presented in IMD4 due to its higher spatial resolution and to the higher density of rainfall stations used for its development.

Monitoring the evolution of drought conditions over Africa

Droughts have been found to have serious repercussions on humans, animals, and plants’ lives and they are likely to intensify under increasing global mean temperature. Monitoring drought conditions help in designing appropriate adaptations and mitigation strategies. This paper monitors the evolution of drought conditions in Africa over the past 30 years and the potential repercussions posed by this disaster event. We analyze and compare trends in surface temperatures, precipitation, soil moisture, Outgoing Longwave Radiation (OLR), and Palmer Drought Severity Index (PDSI). We use the NCEP/NCAR Reanalysis, the University of Delaware, the Climate Prediction Center (CPC), and the DAI PDSI gridded data for the period 1984-2014. Results from the NCEP/NCAR, University of Delaware, CPC, and the DAI PDSI gridded data show an increasingly warmer, drier, and less cloudy Sub-Saharan climate but with an intensification of the West African monsoon rainfall. Moreover, more than 80% of the continent shows strong evidence of droughts with an average increase in drought severity index. These conditions will likely have a negative effect on the agricultural sector which accounts for more than 70% of the Gross Domestic Product (GDP) of this region thereby posing a serious threat to regional food security. We recommend the research into and the development of new crop varieties that can tolerate higher temperatures and need less water. Additionally, our findings can also be used in Sub-Saharan Africa’s water management systems.

Estimating and predicting snakebite risk in the Terai region of Nepal through a high-resolution geospatial and One Health approach

Most efforts to understand snakebite burden in Nepal have been localized to relatively small areas and focused on humans through epidemiological studies. We present the outcomes of a geospatial analysis of the factors influencing snakebite risk in humans and animals, based on both a national-scale multi-cluster random survey and, environmental, climatic, and socio-economic gridded data for the Terai region of Nepal. The resulting Integrated Nested Laplace Approximation models highlight the importance of poverty as a fundamental risk-increasing factor, augmenting the snakebite odds in humans by 63.9 times. For animals, the minimum temperature of the coldest month was the most influential covariate, increasing the snakebite odds 23.4 times. Several risk hotspots were identified along the Terai, helping to visualize at multiple administrative levels the estimated population numbers exposed to different probability risk thresholds in 1 year. These analyses and findings could be replicable in other countries and for other diseases.

Distribution Matters: Sensitivity of Gridded Data of Population and Economic Conditions to Global Water Scarcity Assessment

Availability of water per capita is among the most fundamental water-scarcity indicators and has been used extensively in global grid-based water resources assessments. Recently, it has been extended to include the economic aspect, a proxy of the capability for water management. We applied the extended index globally under SSP–RCP scenarios using gridded population and economic conditions from two independent sources and unexpectedly found that the gridded data were significantly sensitive to global water-scarcity assessment. One projection assumed urban concentration of population and assets, whereas the other assumed dispersion. In analyses using multiple SSP–RCP scenarios representing a world of sustainability (SSP1–RCP2.6), regional rivalry (SSP3–RCP7.0), and fossil fuel development (SSP5–RCP8.5) in the future, multiple GCMs, and two gridded datasets showed that the water-scarce population ranges from 0.32–665 million. Uncertainties in the SSP–RCP and GCM scenarios were 6.58–489 million and 0.68–315 million, respectively. The population distribution assumption had a similar impact, with an uncertainty of 169–338 million. These results highlight the importance of the subregional distribution of socioeconomic factors for predicting the future global environment.

Inter-annual variability of rainfalls in the Amazon basin and its vicinity

An analysis of the rainfall series (12-month running means) of the 5° × 5° gridded data in the Amazon river basin and its vicinity (15° N – 20° S, 30° - 80° W) indicated that the rainfalls were highly variable both from year to year and from region to region. Correlations with even nearby regions hardly exceeded 0.50, though correlations were better (up to 0.70) in the regions near the eastern coast of Brazil. Moderate relationship with ENSO indices was obtained for the Amazon river basin and the regions to its north, and for NE Brazil, while moderate relationship with South Atlantic SST was obtained for NE Brazil and the region immediately to its west. All other relationships (with 30 hPa wind, North Atlantic Oscillation Index, etc.) were obscure.

Variation In Frequency And Share of Duration of Rainy Days In Iran’s Precipitation

In any region, climate change can be manifested in the form of various characteristics of climatic elements. To investigate the possible precipitation variations, as a sign of climate change in Iran, in the present study, the frequency of duration of rainy days was examined as a precipitation characteristic. To this end, gridded data of precipitation were used for the period of 1971-2016, and days with the precipitation of more than 1 mm were considered as rainy days. Considering the frequency of the rainy days, it was revealed that during the study period, one to thirty six-day duration of precipitation occurred in the country. One-day duration had the highest frequencies and covered the vastest area, while thirty six-day duration had the lowest frequencies and covered the smallest area. Accordingly, the one-day duration played the most significant role in annual precipitation. The share of these types of rainfalls in the low-precipitation parts of the country was more than 80% and in some areas. The findings also revealed that there was an increasing frequency of short-term, especially two-day duration, in large parts of the country, and a decrease in the long-term duration. The results showed that latitude and longitude, respectively, had the most significant impact on the frequency distribution of the duration of rainy days. Latitude had a direct effect (except in terms of the share of one-day duration of annual precipitation) and longitude had an inverse effect (except in the share of one-day duration of annual precipitation).

JOANNE: Joint dropsonde Observations of the Atmosphere in tropical North atlaNtic meso-scale Environments

As part of the EUREC4A field campaign which took place over the tropical North Atlantic during January–February 2020, 1215 dropsondes from the HALO and WP-3D aircraft were deployed through 26 flights to characterize the thermodynamic and dynamic environment of clouds in the trade-wind regions. We present JOANNE (Joint dropsonde Observations of the Atmosphere in tropical North atlaNtic meso-scale Environments), the dataset that contains these dropsonde measurements and the products derived from them. Along with the raw measurement profiles and basic post-processing of pressure, temperature, relative humidity and horizontal winds, the dataset also includes a homogenized and gridded dataset with 10 m vertical spacing. The gridded data are used as a basis for deriving diagnostics of the area-averaged mesoscale circulation properties such as divergence, vorticity, vertical velocity and gradient terms, making use of sondes dropped at regular intervals along a circular flight path. A total of 85 such circles, ∼ 222 km in diameter, were flown during EUREC4A. We describe the sampling strategy for dropsonde measurements during EUREC4A, the quality control for the data, the methods of estimation of additional products from the measurements and the different post-processed levels of the dataset. The dataset is publicly available (https://doi.org/10.25326/246, George et al., 2021b) as is the software used to create it (https://doi.org/10.5281/zenodo.4746312, George, 2021).

Evaluation of hydrological modeling using climatic station and gridded precipitation dataset

The conventional rainfall data estimates are relatively accurate at some points of the region. The interpolation of such type of data approximates the actual rainfield however in data scarce regions; the resulted rainfield is the rough estimate of the actual rainfall events. In data scarce regions like Indus basin Pakistan, the data obtained through remote sensing can be very useful. This research evaluates two types of gridded data i.e., European Reanalysis (ERA) interim and Japanese Reanalysis 55 years (JRA-55) along with the climatic station data for three small dams in Pakistan. Since no measured flow data is available at these dams, the nearest possible catchments where flow data is available are calibrated and the calibrated parameters of these catchments are then used in actual dams for simulating the flow from all the three types of data using Soil and Water Assessment Tool (SWAT). The results of the comparison of gridded and rainguage precipitation shows that gridded data highly overestimates the climatic station data. Similar results were observed in the comparison of flow simulated by SWAT model. The Peak flood calculated from JRA-55 overestimates while the Era-Interim peak floods are comparable to that of climatic stations in two of the three catchments.