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Dual-frequency precipitation radar (DPR) on the Core GPM satellite provides spaceborne three-dimensional observations of precipitation fields and surface rainfall rate with quasi-global coverage. The present study evaluates the behavior of liquid precipitation intensity, radar reflectivity factor (ZKu and ZKa) and drop size distribution (DSD) parameters (weighted mean diameter Dm and intercept parameter Nw) of the GPM DPR-derived products, version 07, from 2014 to 2023. Observations from seven Parsivel disdrometers located in different topographic zones in the Western Mediterranean are taken as ground references. Four matching techniques between satellite estimates and ground level observations were tested, and the best results were found for the so-called optimal comparison approach. Overall, GPM DPR products captured the variability of the observed DSD well at different rainfall intensities. However, overestimation of the mean Dm and underestimation of the mean Nw were observed, being much more sensitive to errors in drop diameters larger than 1.5 mm. Moreover, the lowest errors were found for radar reflectivity factor and Dm, and the highest for Nw and rainfall rate. In addition, the GPM DPR convective and stratiform classification was tested, and a substantial overestimation of stratiform cases compared to disdrometer observations were found. [ABSTRACT FROM AUTHOR]

Variable camber wing technology stands out as the most promising morphing technology currently available in green aviation. Despite the ongoing advancements in smart materials and compliant structures, they still fall short in terms of driving force, power, and speed, rendering mechanical structures based on kinematics the preferred choice for large long-range civilian aircraft. In line with this principle, this paper introduces a linkage-based variable camber trailing edge design approach. Covering coordinated design, internal skeleton design, flexible skin design, and drive structure design, the method leverages a two-dimensional supercritical airfoil to craft a seamless, continuous two-dimensional wing full-size variable camber trailing edge structure, boasting a 2.7 m span and 4.3 m chord. Given the significant changes in aerodynamic load direction, ground tests under cruise load utilize a tracking-loading system based on tape and lever. Results indicate that the designed single-degree-of-freedom Watt I mechanism and Stephenson III drive mechanism adeptly accommodate the slender trailing edge of the supercritical airfoil. Under a maximum cruise vertical aerodynamic load of 17,072 N, the structure meets strength requirements when deflected to 5°. The research in this paper can provide some insights into the engineering design of variable camber wings. [ABSTRACT FROM AUTHOR]

Biophysical changes in the Arctic and boreal zones drive shifts in vegetation, such as increasing shrub cover from warming soil or loss of living mat species due to fire. Understanding current and future responses to these factors requires mapping vegetation at a fine taxonomic resolution and landscape scale. Plants vary in size and spectral signatures, which hampers mapping of meaningful functional groups at coarse spatial resolution. Fine spatial grain of remotely sensed data ( 50 % of the independent ground cover estimation and > 84 % accuracy in estimating validation pixels. We explored the impact of spectral resolution on PFT mapping by including vegetation indices and a gradient of narrow (5 nm) to wide (50 nm) band features in our classification models across. Vegetation indices were the most important predictors for classifying PFTs, while including band features improved models, with narrow and wide bandwidths having similar importance but models with wide bandwidths performing slightly better. We conclude that Arctic and boreal PFT reflectance can be pooled across sites for mapping with relatively few labeled pixels. Underfit, simple algorithms outperformed deep learning, at least with these small sample sizes, in classifying PFTs by balancing bias and variance. Future work should aim to increase the number of labeled pixels and the detail of labels to further improve mapping taxonomic precision.

Dual-frequency precipitation radar (DPR) on the Core GPM satellite provides spaceborne three-dimensional observations of precipitation fields and surface rainfall rate with quasi-global coverage. The present study evaluates the behavior of liquid precipitation intensity, radar reflectivity factor (ZKu and ZKa) and drop size distribution (DSD) parameters (weighted mean diameter Dm and intercept parameter Nw) of the GPM DPR-derived products, version 07, from 2014 to 2023. Observations from seven Parsivel disdrometers located in different topographic zones in the Western Mediterranean are taken as ground references. Four matching techniques between satellite estimates and ground level observations were tested, and the best results were found for the so-called optimal comparison approach. Overall, GPM DPR products captured the variability of the observed DSD well at different rainfall intensities. However, overestimation of the mean Dm and underestimation of the mean Nw were observed, being much more sensitive to errors in drop diameters larger than 1.5 mm. Moreover, the lowest errors were found for radar reflectivity factor and Dm, and the highest for Nw and rainfall rate. In addition, the GPM DPR convective and stratiform classification was tested, and a substantial overestimation of stratiform cases compared to disdrometer observations were found.

Variable camber wing technology stands out as the most promising morphing technology currently available in green aviation. Despite the ongoing advancements in smart materials and compliant structures, they still fall short in terms of driving force, power, and speed, rendering mechanical structures based on kinematics the preferred choice for large long-range civilian aircraft. In line with this principle, this paper introduces a linkage-based variable camber trailing edge design approach. Covering coordinated design, internal skeleton design, flexible skin design, and drive structure design, the method leverages a two-dimensional supercritical airfoil to craft a seamless, continuous two-dimensional wing full-size variable camber trailing edge structure, boasting a 2.7 m span and 4.3 m chord. Given the significant changes in aerodynamic load direction, ground tests under cruise load utilize a tracking-loading system based on tape and lever. Results indicate that the designed single-degree-of-freedom Watt I mechanism and Stephenson III drive mechanism adeptly accommodate the slender trailing edge of the supercritical airfoil. Under a maximum cruise vertical aerodynamic load of 17,072 N, the structure meets strength requirements when deflected to 5°. The research in this paper can provide some insights into the engineering design of variable camber wings.

Ground validation of remote sensing soil moisture requires ground measurements corresponding to the pixel scale. To date, there is still a lack of simple, fast and reasonable methods for soil moisture measurement at pixel scale between point measurements and remote sensing observations. In this study, a measurement method of soil moisture using ground-penetrating radar (GPR) was proposed for pixel scale. We used a PulseEKKOTM PRO GPR system with 250 MHz antennas to measure soil moisture by Fixed Offset (FO) method in four 30 × 30 m2 plots chosen from the desert steppe. This study used a random combination method to analyze soil moisture measurements acquired by different numbers of GPR survey lines. The results showed that two survey lines of GPR would be sufficient under confidence level of 90% with the relative error of 7%, and four survey lines of GPR would be eligible under confidence level of 95% with the relative error of 5% for each plot. GPR measurement can reproduce the spatial distribution of soil moisture with higher resolution and smaller error, especially when two and four survey lines are designed in cross shape and grid shape, respectively. The method was applied to ground validation for the soil moisture from Landsat 8, showing the advantages of stable relative errors, less contingency and reliable evaluation when compared to point measurements. This method is fast and convenient and not limited to a certain pixel, and thus largely benefits the scale matching of remote sensing results and field measurements in ground validation. [ABSTRACT FROM AUTHOR]

This study is a preliminary assessment of the latest version of the Global Satellite Measurement of Precipitation (GSMaP version 08) data, which were released in December 2021, for the Indonesian Maritime Continent (IMC), using rain gauge (RG) observations from December 2021 to June 2022. Assessments were carried out with 586 rain gauge (RG) stations using a point-to-pixel approach through continuous statistical and contingency table metrics. It was found that the coefficient correlation (CC) of GSMaP version 08 products against RG observations varied between low (CC = 0.14–0.29), moderate (CC = 0.33–0.45), and good correlation (CC = 0.72–0.75), for the hourly, daily, and monthly scales with a tendency to overestimate, indicated by a positive relative bias (RB). Even though the correlation of hourly data is still low, GSMaP can still capture diurnal patterns in the IMC, as indicated by the compatibility of the estimated peak times for the precipitation amount and frequency. GSMaP data also manage to observe heavy rainfall, as indicated by the good of detection (POD) values for daily data ranging from probability 0.71 to 0.81. Such a good POD value of daily data is followed by a relatively low false alarm ratio (FAR) (FAR < 0.5). However, the GSMaP overestimates light rainfall (R < 1 mm/day); as a consequence, it overestimates the consecutive wet days (CWD) and number of days with rainfall ≥ 1 mm (R1mm) indices, and underestimates the consecutive dry days (CDD) extreme rain index. GSMaP daily data accuracy depends on IMC's topographic conditions, especially for GSMaP real-time data. Of all GSMaP version 08 products evaluated, outperformed post-real-time non-gauge-calibrated (GSMaP_MVK), and followed by post-real-time gauge-calibrated (GSMaP_Gauge), near-real-time gauge-calibrated (GSMaP_NRT_G), near-real-time non-gauge-calibrated (GSMaP_NRT), real-time gauge-calibrated (GSMaP_Now_G), and real-time non-gauge-calibrated (GSMaP_Now). Thus, GSMaP near-real-time data have the potential for observing rainfall in IMC with faster latency. [ABSTRACT FROM AUTHOR]

The ground validation of satellite-based precipitation products (SPPs) is very important for their hydroclimatic application. This study evaluated the performance assessment of four soil moisture-based SPPs (SM2Rain, SM2Rain- ASCAT, SM2Rain-CCI, and GPM-SM2Rain). All data of SPPs were compared with 64 weather stations in Pakistan from January 2005 to December 2020. All SPPs estimations were evaluated on daily, monthly, seasonal, and yearly scales, over the whole spatial domain, and at point-to-pixel scale. Widely used evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (rBias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI) were evaluated for performance analysis. The results of our study signposted that: (1) On a monthly scale, all SPPs estimations were in better agreement with gauge estimations as compared to daily scales. Moreover, SM2Rain and GPM-SM2Rain products accurately traced the spatio-temporal variability with CC >0.7 and rBIAS within the acceptable range (±10) of the whole country. (2) On a seasonal scale (spring, summer, winter, and autumn), GPM-SM2Rain performed more satisfactorily as compared to all other SPPs. (3) All SPPs performed better at capturing light precipitation events, as indicated by the Probability Density Function (PDF); however, in the summer season, all SPPs displayed considerable over/underestimates with respect to PDF (%). Moreover, GPM-SM2RAIN beat all other SPPs in terms of probability of detection. Consequently, we suggest the daily and monthly use of GPM-SM2Rain and SM2Rain for hydro climate applications in a semi-arid climate zone (Pakistan). [ABSTRACT FROM AUTHOR]

Peatland surface motion derived from satellite-based Interferometry of Synthetic Aperture Radar (InSAR) is potentially a proxy for groundwater level variations and greenhouse gas emissions from peat soils. Ground validation of these motions at equivalent temporal resolution has proven problematic, either because of limitations of traditional surveying methods or because of limitations with past InSAR time-series approaches. Novel camera-based instrumentation has enabled in-situ measurement of peat surface from mid-2019 to mid-2022 at two large temperate raised bogs undergoing restoration – Cors Fochno and Cors Caron, in mid-Wales, United Kingdom. The cameras provided continuous measurements at sub-millimetre precision and sub-daily temporal resolution. From these data and Sentinel-1 acquisitions spanning mid-2015 to early-2023, we demonstrate that accurate peat surface motion can be derived by InSAR when a combination of interferometric networks with long and short temporal baselines is used. The InSAR time series data closely match the in-situ data at both bogs, and in particular recover well the annual peat surface oscillations of 10-40 mm. Pearson's values for the point-wise correlation of in-situ and InSAR displacements are 0.8–0.9, while 76% of differences are < ±5 mm and 93% are < ±10 mm. RMSE values between multi-annual in-situ and InSAR peat surface displacement rates are ~7 mm·yr−1 and decrease to ∼3.5 mm for individual peat surface motion measurements. Larger differences mainly occur during drought periods. Multi-annual displacement velocities rates based on InSAR indicate long-term subsidence at Cors Caron (maximum −7 mm·yr−1), while Cors Fochno exhibits subsidence at the centre and uplift at the margins (−9 mm·yr−1 to +5 mm·yr−1). Because of the annual peat surface oscillations, however, more robust ground validation of the long-term peat surface motion rates derived from InSAR requires longer time-series of in-situ measurements than are presently available. Nonetheless, the InSAR-derived surface motion rates correlate well spatially with both peat dome elevation and peat thickness. In addition, the annual oscillations in surface motion are synchronous with or lag slightly behind groundwater level changes. A coarse ratio of 10:1 is observed between annual changes in groundwater level and peat surface displacement. Satellite-based InSAR derived from a fusion of short- and long-term temporal baseline networks can thus enable accurate monitoring of hydrologically driven surface motions of moderately degraded to intact temperate raised peatlands. • New InSAR method to retrieve accurate surface motions at temperate peatlands • InSAR-derived motions validated on raised peatlands with novel camera-based sensors. • Validation of InSAR data to mm-level accuracy at equivalent temporal resolution • "Bog breathing" from annual changes in groundwater levels seen from space via InSAR. • Method has potential for better monitoring and verification of peatland restoration. [ABSTRACT FROM AUTHOR]

Ground validation of remote sensing soil moisture requires ground measurements corresponding to the pixel scale. To date, there is still a lack of simple, fast and reasonable methods for soil moisture measurement at pixel scale between point measurements and remote sensing observations. In this study, a measurement method of soil moisture using ground-penetrating radar (GPR) was proposed for pixel scale. We used a PulseEKKOTM PRO GPR system with 250 MHz antennas to measure soil moisture by Fixed Offset (FO) method in four 30 × 30 m2 plots chosen from the desert steppe. This study used a random combination method to analyze soil moisture measurements acquired by different numbers of GPR survey lines. The results showed that two survey lines of GPR would be sufficient under confidence level of 90% with the relative error of 7%, and four survey lines of GPR would be eligible under confidence level of 95% with the relative error of 5% for each plot. GPR measurement can reproduce the spatial distribution of soil moisture with higher resolution and smaller error, especially when two and four survey lines are designed in cross shape and grid shape, respectively. The method was applied to ground validation for the soil moisture from Landsat 8, showing the advantages of stable relative errors, less contingency and reliable evaluation when compared to point measurements. This method is fast and convenient and not limited to a certain pixel, and thus largely benefits the scale matching of remote sensing results and field measurements in ground validation.

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The cross-validation of radars in a network is important in making consistent retrievals across the domain and assuring the product quality. During the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign, two C-band radars, namely the Colorado State University C-band Hydrological Instrument for Volumetric Observations (CSU-CHIVO) and the C-band Scanning ARM Precipitation Radar (CSAPR-2), were deployed near the Sierras de Cordoba in Argentina, a region known for having some of the most intense severe weather in the world. In addition to these two radars, the operational radar of the Cordoba City, the Radar Meteorologico Argentino 1 (RMA-1), adds another instrument to the RELAMPAGO network. This study presents an intercomparison between the RELAMPAGO C-band radars using the GPM spaceborne radar as a common reference. A method to bring ground-based radars into better agreement is also proposed. Moreover, the attenuation correction for the C-band radar is studied in the context of intercomparing two radars. The attenuation coefficients are computed for the RELAMPAGO domain using the local disdrometers deployed during the campaign. After the attenuation correction, CSU-CHIVO, CSAPR-2, and RMA-1 compare well with GPM-DPR with a high correlation and bias less than 1 dB. [ABSTRACT FROM AUTHOR]

Rare and endemic species comprise globally a priority conservation concern in view of being at a higher risk of extinction. Recording the occurrence data for such species, especially in hardly accessible alpine habitats, is a rather challenging task. Modelling serves as effective tool for predicting habitat suitability and practising artificial introductions for such species with encouraging conservation implications. We used Maxent modelling to predict the habitats suitable for Lagotis cashmiriana (ROYLE) RUPR., a threatened species endemic to Kashmir Himalaya. Our modelling approach consisted of two rounds of modelling and one round of ground validations. The first round of modelling was based on the published literature and herbarium records and the second round included the new records that were obtained from ground validations based on first model predictions. Through this approach, we were able to identify several new populations of L. cashmiriana and reported a significant increase in its range size. We also identified the suitable areas for reintroduction of L. cashmiriana in the western Himalayan region after identifying a broad range of environmental factors that determine the distribution of this species.

This study is a preliminary assessment of the latest version of the Global Satellite Measurement of Precipitation (GSMaP version 08) data, which were released in December 2021, for the Indonesian Maritime Continent (IMC), using rain gauge (RG) observations from December 2021 to June 2022. Assessments were carried out with 586 rain gauge (RG) stations using a point-to-pixel approach through continuous statistical and contingency table metrics. It was found that the coefficient correlation (CC) of GSMaP version 08 products against RG observations varied between low (CC = 0.14–0.29), moderate (CC = 0.33–0.45), and good correlation (CC = 0.72–0.75), for the hourly, daily, and monthly scales with a tendency to overestimate, indicated by a positive relative bias (RB). Even though the correlation of hourly data is still low, GSMaP can still capture diurnal patterns in the IMC, as indicated by the compatibility of the estimated peak times for the precipitation amount and frequency. GSMaP data also manage to observe heavy rainfall, as indicated by the good of detection (POD) values for daily data ranging from probability 0.71 to 0.81. Such a good POD value of daily data is followed by a relatively low false alarm ratio (FAR) (FAR < 0.5). However, the GSMaP overestimates light rainfall (R < 1 mm/day); as a consequence, it overestimates the consecutive wet days (CWD) and number of days with rainfall ≥ 1 mm (R1mm) indices, and underestimates the consecutive dry days (CDD) extreme rain index. GSMaP daily data accuracy depends on IMC’s topographic conditions, especially for GSMaP real-time data. Of all GSMaP version 08 products evaluated, outperformed post-real-time non-gauge-calibrated (GSMaP_MVK), and followed by post-real-time gauge-calibrated (GSMaP_Gauge), near-real-time gauge-calibrated (GSMaP_NRT_G), near-real-time non-gauge-calibrated (GSMaP_NRT), real-time gauge-calibrated (GSMaP_Now_G), and real-time non-gauge-calibrated (GSMaP_Now). Thus, GSMaP near-real-time data have the potential for observing rainfall in IMC with faster latency.

The ground validation of satellite-based precipitation products (SPPs) is very important for their hydroclimatic application. This study evaluated the performance assessment of four soil moisture-based SPPs (SM2Rain, SM2Rain- ASCAT, SM2Rain-CCI, and GPM-SM2Rain). All data of SPPs were compared with 64 weather stations in Pakistan from January 2005 to December 2020. All SPPs estimations were evaluated on daily, monthly, seasonal, and yearly scales, over the whole spatial domain, and at point-to-pixel scale. Widely used evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (rBias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI) were evaluated for performance analysis. The results of our study signposted that: (1) On a monthly scale, all SPPs estimations were in better agreement with gauge estimations as compared to daily scales. Moreover, SM2Rain and GPM-SM2Rain products accurately traced the spatio-temporal variability with CC >0.7 and rBIAS within the acceptable range (±10) of the whole country. (2) On a seasonal scale (spring, summer, winter, and autumn), GPM-SM2Rain performed more satisfactorily as compared to all other SPPs. (3) All SPPs performed better at capturing light precipitation events, as indicated by the Probability Density Function (PDF); however, in the summer season, all SPPs displayed considerable over/underestimates with respect to PDF (%). Moreover, GPM-SM2RAIN beat all other SPPs in terms of probability of detection. Consequently, we suggest the daily and monthly use of GPM-SM2Rain and SM2Rain for hydro climate applications in a semi-arid climate zone (Pakistan).

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, we validate precipitation estimates remotely sensed by the Tropical Rainfall Measuring Mission (TRMM) at monthly and seasonal timescales, during the period 1998–2015, by calculating and analyzing diverse error metrics between the 3B43 V7 product and in situ measurements from 1,180 rain gauges over Colombia, of which at least 987 are fully independent of TRMM. We explore the existence of spatiotemporal patterns to assess the performance of 3B43 V7 over the five major natural regions of Colombia: Caribbean, Pacific, Andes, Orinoco and Amazon. The results show that 3B43 V7 product is able to capture the phase of the annual cycle of monthly mean precipitation, but the performance is not good for the amplitude, in particular over the Andes and Pacific regions owing to complex climatic and topographic conditions. In general, 3B43 V7 exhibits good performance in the low‐lying and plain Amazon, Orinoco and Caribbean regions. Over the Andes region, characterized by complex topography, overestimation errors are identified [root mean squared error (RMSE) ≥83.59 mm·month−1 and relative bias (BIAS) ≥4.69%], whereas the extremely wet rainfall regime of the Pacific region is largely underestimated (RMSE ≥253.52 mm ·month−1 and BIAS ≤−11.75%). These errors are greater during the wet seasons when the metrics reach worse scores than those reported in similar studies worldwide. Occurrence analyses showed that 3B43 V7 misses very frequent light rainfall events and less frequent but very heavy storms, which contribute to the overall underestimation (overestimation) observed over the Pacific (Andes) region. The error characteristics identified and quantified in this study confirm the well‐documented limitations of remote precipitation sensing and constitute a warning about major challenges that complex climatic and physiographic features can impose on satellite rainfall missions. [ABSTRACT FROM AUTHOR]

Precipitation consists of many types of hydrometeors, such as raindrops, ice crystals, graupel, and hail. Due to their impacts, graupel and hail (GH) have received particular attention in the literature. Global Precipitation Measurement (GPM) dual-frequency radar (DPR) has proved to be a very reliable system for global precipitation retrievals. This paper aims to develop a GH identification algorithm for GPM DPR. This algorithm is constructed using a precipitation type index (PTI) defined for DPR. The PTI is effective in separating hydrometeor types and is calculated using measurements of reflectivity, dual-frequency ratio, and storm top height data. The output of the algorithm is a Boolean product representing the existence of graupel or hail along with the vertical profile for each Ku- and Ka-band matched footprint. Cross validation is performed with the Weather Service Radar (WSR-88D) network over continental United States as well as during the Remote sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) experiment. Evaluation of the GH identification algorithm is performed on a global basis, which illustrates promising comparisons with the global lightning and hail precipitation maps generated using radar and radiometer. [ABSTRACT FROM AUTHOR]

Accurate precipitation observations are crucial for water resources management and as inputs for a gamut of hydrometeorological applications. Precipitation data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) have recently been widely used to complement traditional rain gauge systems. However, the satellite precipitation data needs to be validated before being widely used in the applications and this is still missing over the Indonesian maritime continent (IMC). We conducted a validation of the IMERG product version 6 for this region. The evaluation was carried out using gauge data in the period from 2016 to 2020 for three types of IMERG: Early (E), Late (L), and Final (F) from annual, monthly, daily and hourly data. In general, the annual and monthly data from IMERG showed a good correlation with the rain gauge, with the mean correlation coefficient (CC) approximately 0.54–0.78 and 0.62–0.79, respectively. About 80% of stations in the IMC area showed a very good correlation between gauge data and IMERG-F estimates (CC = 0.7–0.9). For the daily assessment, the CC value was in the range of 0.39 to 0.44 and about 40% of stations had a correlation of 0.5–0.7. IMERG had a fairly good ability to detect daily rain in which the average probability of detection (POD) for all stations was above 0.8. However, the false alarm ratio (FAR) value is quite high (

This study has evaluated Satellite Based Precipitation (SBP) products at key basins in Bolivia. Three basins of national interest were selected at different ecological steps: highlands, valleys and lowlands. GSMaP and CHIRPS products were tested showing promising detection abilities especially in regions with high elevation. At highlands the correlation coefficients of both SBP products showed values from 0.915 to 0.976. On the other hand, at lowlands, the coefficient values ranged from 0.895 to 0.926. Then, the combined products of GSMaP and CHIRPS with national rain gauge network were obtained using correction factors iteratively at sub-basin scale. The national sub-basins coding system of was used. These combined products so-called GS and CH showed a notorious improvement from 0.996 to 0.999 in relation to raw SBP products. At lowland regions results showed the largest improvement percentage in comparison with the other regions suggesting that higher intensities play a key role for correction scheme. The proposed scheme is feasible to implement with other SBP products and in other regions. The precipitation at sub-basins scale allows performing water balance, hydrological studies, biophysical assessments, water management planning among other applications. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The CANYVAL-C (CubeSat Astronomy by NASA and Yonsei using a virtual telescope alignment for coronagraph) is a space science demonstration mission that involves taking several images of the solar corona with two CubeSats—1U CubeSat (Timon) and 2U CubeSat (Pumbaa)—in formation flying. In this study, we developed and evaluated structural and thermal designs of the CubeSats Timon and Pumbaa through finite element analyses, considering the nonlinearity effects of the nylon wire of the deployable solar panels installed in Pumbaa. On-orbit thermal analyses were performed with an accurate analytical model for a visible camera on Timon and a micro propulsion system on Pumbaa, which has a narrow operating temperature range. Finally, the analytical models were correlated for enhancing the reliability of the numerical analysis. The test results indicated that the CubeSats are structurally safe with respect to the launch environment and can activate each component under the space thermal environment. The natural frequency of the nylon wire for the deployable solar panels was found to increase significantly as the wire was tightened strongly. The conditions of the thermal vacuum and cycling testing were implemented in the thermal analytical model, which reduced the differences between the analysis and testing.

The high relevance of satellites for collecting information regarding precipitation at global scale implies the need of a continuous validation of satellite products to ensure good data quality over time and to provide feedback for updating and improving retrieval algorithms. However, validating satellite products using measurements collected by sensors at ground is still a challenging task. To date, the Dual-frequency Precipitation Radar (DPR) aboard the Core Satellite of the Global Precipitation Measurement (GPM) mission is the only active sensor able to provide, at global scale, vertical profiles of rainfall rate, radar reflectivity, and Drop Size Distribution (DSD) parameters from space. In this study, we compare near surface GPM retrievals with long time series of measurements collected by seven laser disdrometers in Italy since the launch of the GPM mission. The comparison shows limited differences in the performances of the different GPM algorithms, be they dual- or single-frequency, although in most cases, the dual-frequency algorithms present the better performances. Furthermore, the agreement between satellite and ground-based estimates depends on the considered precipitation variable. The agreement is very promising for rain rate, reflectivity factor, and the mass-weighted mean diameter (Dm), while the satellite retrievals need to be improved for the normalized gamma DSD intercept parameter (Nw).

In this paper, we give an extension of the functional regression concurrent model to the case of spatially correlated errors. We propose estimating the spatial correlation structure by using functional geostatistics. The estimation of the regression parameters is carried out by feasible generalized least squares. This modeling approach is motivated by the problem of validating rainfall data retrieved from satellite sensors. In this sense, we use the methodology to study the relationship between satellite and ground rainfall time series recorded in 82 weather stations from Department of Valle del Cauca, Colombia. The model obtained allows predicting pentadal rainfall curves in many sites of the region of interest by using as input the satellite information. A residual analysis shows a good performance of the methodology proposed. [ABSTRACT FROM AUTHOR]

This paper investigates the accuracy of the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (GPM), which provides merged microwave and infrared satellite precipitation estimates, over the contiguous US. The study focuses on diurnal variations in precipitation during a two‐year summer period (June–August, 2014–2015). The normalized amplitude and phase of the diurnal cycle of IMERG are evaluated against those of ground reference from the Multi‐Radar/Multi‐Sensor system in terms of precipitation amount, frequency and intensity on a 1°/1 hr scale. IMERG well captures large‐scale regional features of the diurnal cycle of precipitation and overall agrees well with the reference for both diurnal and semidiurnal variations. The comparison results indicate that the IMERG precipitation estimates can be a reliable alternative to ground‐based measurements even at the subdaily scale; however, region‐specific data discrepancies are still observed. For instance, we reveal that IMERG substantially overestimates normalized amplitude of diurnal precipitation in the central US, while IMERG tends to underestimate diurnal variations over the mountain regions in the western and eastern US. In terms of phase, we find a significant difference in the timing of peak precipitation between convective and stratiform regions of mesoscale convective systems (MCSs) over the Great Plains. This time shift is more apparent during the mature and dissipation stages of MCSs, which lead to relatively early peaks in the diurnal cycle of precipitation from IMERG. This phase bias implies a higher sensitivity of IMERG towards the convective regions of MCSs, supposedly because of the brightness temperature depression coming from ice particles aloft sampled by spaceborne passive microwave sensors. Such discrepancy between the actual and satellite‐estimated precipitation timing can be challenging, e.g. when the satellite data are used to study subdaily precipitation processes or to validate numerical simulations. Consequently, our assessment of the IMERG performances highlights the need for improvements in the IMERG system. We study the accuracy of GPM IMERG precipitation estimates over the US using ground reference from radar and gauge‐based MRMS, with emphasis on diurnal variations of precipitation. Regional‐specific biases are found in normalized amplitude and phase of the diurnal cycle of precipitation from IMERG. Possible sources of biases are discussed based on regional difference in precipitation processes. [ABSTRACT FROM AUTHOR]

Among specific applications of species distribution models (SDMs), the use of SDMs probabilistic maps for guiding field surveys is increasingly applied. This approach is particularly used for poorly known and/or cryptic species in order to better assess their distribution. One of the most interesting aspects of these applications is that predictions could be clearly validated by real data, subsequently obtained in the field. Despite this important difference from other applications, to our knowledge, the efficiency of different algorithms, metrics for model evaluation and algorithm-specific settings have not yet been sufficiently investigated. This research performs a literature survey to investigate which species, study area characteristics, variables, techniques and settings were used or suggested by previous authors. We then applied the most common approaches to guide field surveys for a set of 70 vascular plants in an endemic-rich area of Sardinia (Italy) of approx. 9000 ha, the flora of which was deeply investigated during the last two years. Our main aims were: (1) to use pre-model records for predicting the potential occurrence of plant species with different sample size, detectability and habitat preference, (2) to apply results for guiding searches for new populations of poorly known species, (3) to calculate the model performance according to independent real presence/absence data (testAUC) and (4) to compare different modelling data input and settings on the testAUC basis. By emphasizing the importance of field verification, both the review and the worked example supported the reliability of SDMs for a wide range of species to understand where a species could potentially be present and therefore to optimise resources for the search of new species localities. This study may help understand and summarise the most applied methodological approaches and to highlight future directions for this practical application. Without underrating the importance of most common recommendations, practitioners are encouraged to test the ability of this SDMs’ application with their own data. Indeed, large gaps in biological groups (e.g. insects) and in regions covered by these kind of studies (e.g. many African and Asian territories) were found. Furthermore, eventual biases due to lack of data, experience or staff, have in this experimental case less irreparable consequences than others, such as conservation assessments based on future projections, which cannot be otherwise adjusted by explicit data from ground validation. [ABSTRACT FROM AUTHOR]

Three-dimensional point data acquired by Terrestrial Lidar Scanning (TLS) is used as ground observation in comparisons with fire severity indices computed from Landsat satellite multi-temporal images through Google Earth Engine (GEE). Forest fires are measured by the extent and severity of fire. Current methods of assessing fire severity are limited to on-site visual inspection or the use of satellite and aerial images to quantify severity over larger areas. On the ground, assessment of fire severity is influenced by the observers’ knowledge of the local ecosystem and ability to accurately assess several forest structure measurements. The objective of this study is to introduce TLS to validate spectral burned ratios obtained from Landsat images. The spectral change was obtained by an image compositing technique through GEE. The 32 plots were collected using TLS in Wood Buffalo National Park, Canada. TLS-generated 3D points were converted to voxels and the counted voxels were compared in four height strata. There was a negative linear relationship between spectral indices and counted voxels in the height strata between 1 to 5 m to produce R2 value of 0.45 and 0.47 for unburned plots and a non-linear relationship in the height strata between 0 to 0.5m for burned plots to produce R2 value of 0.56 and 0.59. Shrub or stand development was related with the spectral indices at unburned plots, and vegetation recovery in the ground surface was related at burned plots. As TLS systems become more cost efficient and portable, techniques used in this study will be useful to produce objective assessments of structure measurements for fire refugia and ecological response after a fire. TLS is especially useful for the quick ground assessments which are needed for forest fire applications.

The Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) core satellite has reflectivity measurements at two different frequency bands, namely, Ku- and Ka-bands. The dual-frequency ratio from these measurements has been used to perform rain-type classification and microphysics retrieval in the current DPR level 2 algorithm. In this paper, a surface snowfall identification algorithm is developed using GPM DPR observations. This algorithm provides a new approach to detect snowfall through radar observations, such as measured dual-frequency ratio. This algorithm is developed using GPM DPR data as well as Atmospheric Radiation Measurement (ARM) X/Ka-band radar data during the snowfall experiment. Several snow events observed by both DPR and ground radars are used in the algorithm validation, showing good comparisons. [ABSTRACT FROM PUBLISHER]

Vegetation indices are widely used as model inputs and for non-destructive estimation of biomass and photosynthesis, but there have been few validation studies of the underlying relationships. To test their applicability on temperate fens and the impact of management intensity, we investigated the relationships between normalized difference vegetation index ( NDVI), leaf area index ( LAI), brown and green above-ground biomass and photosynthesis potential ( PP). Only the linear relationship between NDVI and PP was management independent ( R2 = 0·53). LAI to PP was described by a site-specific and negative logarithmic function ( R2 = 0·07-0·68). The hyperbolic relationship of LAI versus NDVI showed a high residual standard error (s.e.) of 1·71-1·84 and differed between extensive and intensive meadows. Biomass and LAI correlated poorly ( R2 = 0·30), with high species-specific variability. Intensive meadows had a higher ratio of LAI to biomass than extensive grasslands. The fraction of green to total biomass versus NDVI showed considerable noise (s.e. = 0·13). These relationships were relatively weak compared with results from other ecosystems. A likely explanation could be the high amount of standing litter, which was unevenly distributed within the vegetation canopy depending on the season and on the timing of cutting events. Our results show there is high uncertainty in the application of the relationships on temperate fen meadows. For reliable estimations, management intensity needs to be taken into account and several direct measurements throughout the year are required for site-specific correction of the relationships, especially under extensive management. Using NDVI instead of LAI could reduce uncertainty in photosynthesis models. [ABSTRACT FROM AUTHOR]

Red snow algal blooms reduce albedo and increase snowmelt, but little is known of their extent, duration, and radiative forcing. We calibrated an established index by comparing snow algal field spectroradiometer measurements with direct counts of algal cell abundance in British Columbia, Canada. We applied the field calibrated index to Sentinel-2, Landsat-8, and MODIS/Terra images to monitor snow algae on the Vowell and Catamount Glaciers (Purcells, British Columbia) in summer 2020. The maximum extent of snow algal bloom cover was 1.4 and 2.0 km2 respectively, about one third of the total surface area of the two glaciers, making these among the largest contiguous bloom areas yet reported. Blooms were first detected following the onset of above-freezing temperatures in early July and persisted for about two months. Algal abundance increased through July, after which the red snow algal bloom area decreased due to snow cover loss. At their peak in late July the blooms reduced albedo by 0.04 ± 0.01 on average. Snow algae caused an additional 5.25 ± 1.0 × 107 J/m2 of solar energy to be absorbed by the snowpack in July–August, which is enough energy to melt 31.5 cm of snow. This is equivalent to an average snow algal radiative forcing of 8.25 ± 1.6 W/m2 through July and August. Our results suggest that the extent, duration, and radiative forcing of snow algal blooms are sufficient to enhance glacial melt rates. • An existing index for remote sensing snow algal abundance was field calibrated. • Algal blooms covered up to 31% of two considered glaciers in British Columbia. • Blooms began early July, peaked in late July, and persisted until September. • Snow algae accounted for about half the July albedo decline in the bloom area. • Snow algal radiative forcing was enough to melt 31.5 cm of snow. [ABSTRACT FROM AUTHOR]

Surface longwave radiation (SLR) plays an important role in the energy budget of the Earth's climate system. Remote sensing provides various data sources to retrieve SLR on a large scale and with high spatial resolution (e.g., 1 km). Multiple retrieval methods of surface downward longwave radiation (SDLR) based on satellite thermal infrared data produce different retrieval results for the same scenario. Therefore, validating these models is necessary to understand their characteristics and limitations. To this end, ground-based measurements were used to provide independent validation of six widely used SDLR models for clear sky conditions over 41 Baseline Surface Radiation Network (BSRN) stations worldwide. The Wang2020 model had the best overall performance (bias of −5.480 W / m 2, root-mean-square errors [RMSE] of 23.226 W / m 2, R 2 of 0.879), and Tang2008 model had similar retrieval capability. The errors of LST had limited influence on the retrieval accuracy of SDLR models. When using the near-surface air temperature, the retrieval accuracy of the Zhou2007 model was significantly improved with a range of ~9.5 W / m 2 for the RMSE. The uncertainty of TCWV had significant effect on all model performances, wherein the Zhou2007 model had stronger error resilience of TCWV. Moreover, MODIS TIR TCWV data provided better performance than NIR TCWV in most situations, and thereby are preferred to use in the SDLR retrieval. Surface altitude had a lesser impact on SDLR retrieval than terrain effects. All models overestimated SDLR for peak stations in mountainous areas, with biases reaching 56.614 W / m 2 and RMSE reaching 63.909 W / m 2. Land cover type also had a significant effect on retrieval accuracy; model performances were poorer in the desert and barren where atmospheric conditions are extremely dry and hot. Remote sensing SDLR data with high accuracy are needed for hydrological, agricultural, and climate change applications. The results of this study provide a reference for the SDLR retrieval accuracy based on clear-sky models. • Six SDLR models were validated using measurements from 41 BSRN stations worldwide. • Comprehensive validation was performed for different influencing factors. • The Wang2020 model had the best overall performance (RMSE of 23.226 W / m 2). • Near-surface air temperature can significantly improve the accuracy of Zhou2007 model. • MODIS TIR TCWV data are preferred in SDLR retrievals with better performance. [ABSTRACT FROM AUTHOR]

Accurate precipitation observations are crucial for water resources management and as inputs for a gamut of hydrometeorological applications. Precipitation data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) have recently been widely used to complement traditional rain gauge systems. However, the satellite precipitation data needs to be validated before being widely used in the applications and this is still missing over the Indonesian maritime continent (IMC). We conducted a validation of the IMERG product version 6 for this region. The evaluation was carried out using gauge data in the period from 2016 to 2020 for three types of IMERG: Early (E), Late (L), and Final (F) from annual, monthly, daily and hourly data. In general, the annual and monthly data from IMERG showed a good correlation with the rain gauge, with the mean correlation coefficient (CC) approximately 0.54–0.78 and 0.62–0.79, respectively. About 80% of stations in the IMC area showed a very good correlation between gauge data and IMERG-F estimates (CC = 0.7–0.9). For the daily assessment, the CC value was in the range of 0.39 to 0.44 and about 40% of stations had a correlation of 0.5–0.7. IMERG had a fairly good ability to detect daily rain in which the average probability of detection (POD) for all stations was above 0.8. However, the false alarm ratio (FAR) value is quite high (<0.5). For hourly data, IMERG's performance was still poor with CC around 0.03–0.28. For all assessments, IMERG generally overestimated rainfall in comparison with rain gauge. The accuracy of the three types of IMERG in IMC was also influenced by season and topography. The highest and lowest CC values were observed for June–July–August and December–January–February, respectively. However, categorical statistics (POD, FAR and critical success index) did not show any clear seasonal variation. The CC value decreased with higher altitude, but with slight difference for each IMERG type. For all assessments conducted, IMERG-F generally showed the best rainfall observations in IMC, but with slightly difference from IMERG-E and IMERG-L. Thus, IMERG-E and IMERG-L data that had a faster latency than IMERG-F show potential to be used in rainfall observations in IMC. [ABSTRACT FROM AUTHOR]

Highlights: [•] We describe the design and test of an autonomously operating terrestrial laser scanner (ATLS). [•] The ATLS provided reliable, repeatable scans under harsh field conditions. [•] The ATLS accurately quantified 3-D temporal dynamics of plant structure. [•] ATLS data can enable new insights into dynamic ecological processes. [Copyright &y& Elsevier]

Abstract: The Global Precipitation Measurement (GPM) mission is scheduled to fly in the year of 2013 to measure the earth''s precipitation structure. Since the precipitation measurement by the GPM platform will be very similar to its predecessor, the Tropical Rainfall Measuring Mission (TRMM), hence, the development of GPM algorithms to improve precipitation retrievals can be addressed through the lessons learnt from the former TRMM mission in terms of precipitation retrievals and its associated uncertainty. To support the future GPM mission, this paper assesses the performance of the TRMM precipitation estimation using ground-based radars from the GPM validation network. A total of 22 significant overpass instantaneous events from 22 different radar sites has been evaluated in view of different surface and rain type flags. The overall results show that attenuation corrected reflectivity from the TRMM precipitation radar agrees well to the measured reflectivity from ground based radars with correlation coefficients r=0.91 (without frequency adjustment) and r=0.92 (with frequency adjustment). However, the correlation decreases by 10–30%, once the reflectivity are transformed to rainfall rates. The lower correlations on the basis of precipitation estimation by the TRMM are exhibited over the coast than those of ocean and land surface terrain. Taking into account the rain type flags, the analysis shows a poor correlation during convective precipitations, in particular, those retrieved from the TRMM precipitation radar. In contrast, the combined algorithm, which utilizes both radar and microwave imager instrument on-board TRMM, outperforms throughout the analysis, yet, there is a scope to improve the precipitation retrievals. [Copyright &y& Elsevier]

Abstract: Different sources of atmospheric water vapor and temperature profiles were used with a radiative transfer model for retrieving land surface temperature (LST) from thermal infrared remote sensing data with the so-called single channel (SC) method. Retrieved LSTs were compared to concurrent ground measurements over homogeneous rice fields to assess the accuracy of the atmospheric profiles. These included radiosonde balloons launched at the test site near-concurrently to satellite overpasses, re-analysis profiles from the National Centers for Environmental Prediction (NCEP), and satellite sounder products from the Atmospheric Infrared Sounder (AIRS) and the Moderate Imaging Spectroradiometer (MODIS; MOD07 product). SC LSTs were computed for Enhanced Thematic Mapper+ (ETM+), Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER), MODIS, and Advanced Along-Track Scanning Radiometer (AATSR). Results show that radiosonde profiles provided the best agreement between ground-measured and satellite-derived LSTs, with root mean square difference (RMSD) better than 1.0K and biases within ±0.5K for most of the cases. As an alternative to radiosonde profiles, NCEP and MOD07 data yielded reasonable results with RMSDs around 1.0K, although LSTs derived from MOD07 profiles showed a slight overestimation (0.5 to 1.0K) of the ground LSTs. AIRS profiles usually underestimated the ground LSTs by 1 to 2K, probably due to the large temporal gap (2–3h) with the other satellite measurements. We propose a test to assess the suitability of atmospheric profiles applicable to sensors with bands at 11 and 12μm in the split-window. So this test plus the SC method may be called split-window SC algorithm, being significantly different from the simple SC method. It implies the calculation of the difference between the LST derived from both bands (Tig −Tjg), which should be close to zero if the atmospheric profile is accurate and the surface emissivity is well known. A small range of Tig −Tjg values around zero can be set for which the LST derived at 11μm is accurate. Using only the profiles passing the test, LSTs derived from MODIS band 31 agreed with the ground data with a mean bias of −0.1K (ground minus satellite) and RMSD of 0.6K, while AATSR band at 11μm yielded a −0.1K bias and RMSD=0.5K. [Copyright &y& Elsevier]

This study is a forensic investigation of how two well-known satellite rainfall products, available at native scales that are much coarser than that ideally needed for detecting heavy rainfall-induced localized flooding, might have performed for July 2002 flooding in south-central Texas. These two products are: 1) the Global Precipitation Climatology Project (GPCP) rainfall product available at 1° and daily resolution; and 2) the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product 3B42RT available at 0.25 degree and 3 hourly resolution. Both products are considered sufficiently coarse for the flooding that evolved in the typically Hortonian (infiltration-excess) catchments of south-central Texas. An error assessment was performed on these rainfall datasets based on ground validation (GV) data. Compared with the hydrographs of gauge points located within the flooding region, both satellite rainfall products showed promise in detecting the rising and receding trends of the flood events. However, compared with flood inundation maps from Dartmouth Flood Observatory, 3B42RT showed higher agreement in matching with flow accumulation pattern at the subgrid scale. This study shows that merged satellite precipitation products that are emerging today from the constellation of low-earth orbit passive microwave sensors have adequate information content at the native scale for application in providing guidance on localized flooding over ungauged basins. [ABSTRACT FROM AUTHOR]

Abstract: Although the TRMM-based Flood Detection System (FDS) has been in operation in near real-time since 2006, the flood ‘detection’ capability has been validated mostly against qualitative reports in news papers and other types of media. In this study, a more quantitative validation of the FDS over Bangladesh against in situ measurements is presented. Using measured stream flow and rainfall data, the study analyzed the flood detection capability from space for three very distinct river systems in Bangladesh: (1) Ganges– a snowmelt-fed river regulated by upstream India, (2) Brahmaputra – a snow-fed river that is braided, and (3) Meghna – a rain-fed and relatively flashier river. The quantitative assessment showed that the effectiveness of the TRMM-based FDS can vary as a function of season and drainage basin characteristics. Overall, the study showed that the TRMM-based FDS has great potential for flood prone countries like Bangladesh that are faced with tremendous hurdles in transboundary flood management. The system had a high probability of detection overall, but produced increased false alarms during the monsoon period and in regulated basins (Ganges), undermining the credibility of the FDS flood warnings for these situations. For this reason, FDS users are cautioned to verify FDS estimates during the monsoon period and for regulated rivers before implementing flood management practices. Planned improvements by FDS developers involving physically-based hydrologic modeling should transform the system into a more accurate tool for near real-time decision making on flood management for ungauged river basins of the world. [Copyright &y& Elsevier]

1. We evaluated the accuracy of presence-only modelling for predicting the distribution of rare species, when data are scarce and usually collected with sampling biases. We modelled the potential distribution in Portugal of one of the rarest European bats Barbastella barbastellus and subsequently ground-validated predictions by using acoustic transects. 2. We used ecological niche factor analysis (ENFA) and maximum entropy (Maxent) modelling to build distribution models of B. barbastellus, and determined which ecological factors were more relevant for each model. As ENFA only accepts continuous variables, we built one Maxent model using the same variables as ENFA and another using land cover as a categorical variable. 3. Ecological niche factor analysis and both Maxent models predicted similar areas of occurrence in central and northern regions of Portugal, although ENFA predicted suitable habitat over a wider range. Conversely, there was substantial disagreement on the location of high-suitability areas in the south. This could be a consequence of a different choice of important variables made by each model. Native woodland and average temperature were the most relevant variables for Maxent, while in ENFA B. barbastellus was linked to higher altitudes although avoiding production forests and infrastructures. 4. Threshold-independent and -dependent statistics showed that Maxent models outperformed ENFA, probably as a consequence of divergent predictions in the new areas of occurrence. Overall, 15 new B. barbastellus sites were discovered and known distribution was extended c. 100 km to the south. 5. Synthesis and applications. Our results support the use of presence-only modelling as an indispensible tool for survey design as shown by the discovery of B. barbastellus populations outside of the previously known range. ENFA seems to be more suited to determining a species’ potential distribution, although failing to extrapolate it. In contrast, Maxent is better suited to determining a species’ realized distribution. It was successful in predicting occurrence in previously unsurveyed areas and can be recommended as a technique for determination of a conservative distribution for a species. Maxent modelling would greatly aid biodiversity conservation, especially when it is necessary to develop survey plans or first assessments of a species’ distribution. [ABSTRACT FROM AUTHOR]

• GPM-IMERG and WRF are recommended for hydroclimatic applications in Eastern Himalaya. • ERA5, TRMM-3B42RTV7 and IMD-0.25° captured extreme precipitation events satisfactorily. • IMD-0.25° is advised for precipitation trend analysis after suitable bias-correction. • GPPs underestimated (overestimated) precipitation below (above) 3000 masl elevation. • Satellite GPPs captured the seasonal and diurnal cycles with subdued amplitudes. Precipitation plays a key role in shaping land surface processes in Himalaya. It is also the most challenging meteorological variable to model in climate change studies due to inadequate ground data. Gridded Precipitation Products (GPPs) are useful alternatives to ground data but require validation, especially in topographically complex and wet Eastern Himalaya. This study presents a fine-scaled ground validation of eleven GPPs, including five satellite-based (GPM-IMERGV06, TRMM-3B42V7, TRMM-3B42V7RT, CHIRPS-2.0 and PERSIANN-CCS), four reanalysis model-based (ERA5, ERA5-Land, AgERA5, and WRF) and two gauge-interpolated (IMD-0.25° and APHRODITE-2V18) GPPs in Eastern Himalaya. Hourly precipitation data from 27 rain gauges (Gauges) from Sikkim, representing the Eastern Himalayan climatology, is used to statistically validate the GPPs and assess their ability to capture diurnal and seasonal patterns, and extreme events. Overall, GPM-IMERG, WRF, and IMD-0.25° outperformed the other satellite, model, and interpolated GPPs, respectively, in comparison to Gauges. Near real-time TRMM-3B42RTV7 performed better than TRMM-3B42V7 for high-intensity precipitation and large storms, but not in overall performance. CHIRPS-2.0 and PERSIANN-CCS showed the least detectability and highest errors; however, PERSIANN-CCS was better at reproducing known spatial patterns in ground precipitation. The GPPs underestimated (overestimated) precipitation frequencies and volumes below (above) 3000 masl, and high (low) intensities. The highest detectability and lowest errors were observed at mid-elevation (1000-2000 masl), and in monsoon (JJASO) and summer (MAM), whereas winter (NDJF) precipitation was overestimated with high false hits. Strong diurnal cycles in precipitation were observed in Gauges with peaks around late-night-early morning (2300–0300 h) in monsoon and afternoon-evening (1600–1900 h) in summer. Satellite GPPs captured the diurnal cycle in Gauges, albeit with subdued amplitudes, whereas model GPPs failed. Owing to their superior performance and fine spatiotemporal resolutions, GPM-IMERG and WRF are recommended for hydrological studies in Eastern Himalaya. The three ERA5-based products (ERA5, ERA5-Land, AGERA5), along with TRMM-3B42RTV7 and IMD-0.25°, showed the lowest bias in tracking large storms (longer than 5 days) and are advocated for understanding extreme events and in geohazard applications. IMD-0.25° is recommended for precipitation trend and variability analysis, albeit with suitable bias-correction to address the negative biase at high elevations and the positive bias in trans-Himalayan regions. The study improves our understanding of remotely-sensed and spatially-gridded precipitation in the world's highest mountain range, where ground observations are likely to be inadequate for years to come. [ABSTRACT FROM AUTHOR]

The high relevance of satellites for collecting information regarding precipitation at global scale implies the need of a continuous validation of satellite products to ensure good data quality over time and to provide feedback for updating and improving retrieval algorithms. However, validating satellite products using measurements collected by sensors at ground is still a challenging task. To date, the Dual-frequency Precipitation Radar (DPR) aboard the Core Satellite of the Global Precipitation Measurement (GPM) mission is the only active sensor able to provide, at global scale, vertical profiles of rainfall rate, radar reflectivity, and Drop Size Distribution (DSD) parameters from space. In this study, we compare near surface GPM retrievals with long time series of measurements collected by seven laser disdrometers in Italy since the launch of the GPM mission. The comparison shows limited differences in the performances of the different GPM algorithms, be they dual- or single-frequency, although in most cases, the dual-frequency algorithms present the better performances. Furthermore, the agreement between satellite and ground-based estimates depends on the considered precipitation variable. The agreement is very promising for rain rate, reflectivity factor, and the mass-weighted mean diameter (Dm), while the satellite retrievals need to be improved for the normalized gamma DSD intercept parameter (Nw). [ABSTRACT FROM AUTHOR]

The CANYVAL-C (CubeSat Astronomy by NASA and Yonsei using a virtual telescope alignment for coronagraph) is a space science demonstration mission that involves taking several images of the solar corona with two CubeSats—1U CubeSat (Timon) and 2U CubeSat (Pumbaa)—in formation flying. In this study, we developed and evaluated structural and thermal designs of the CubeSats Timon and Pumbaa through finite element analyses, considering the nonlinearity effects of the nylon wire of the deployable solar panels installed in Pumbaa. On-orbit thermal analyses were performed with an accurate analytical model for a visible camera on Timon and a micro propulsion system on Pumbaa, which has a narrow operating temperature range. Finally, the analytical models were correlated for enhancing the reliability of the numerical analysis. The test results indicated that the CubeSats are structurally safe with respect to the launch environment and can activate each component under the space thermal environment. The natural frequency of the nylon wire for the deployable solar panels was found to increase significantly as the wire was tightened strongly. The conditions of the thermal vacuum and cycling testing were implemented in the thermal analytical model, which reduced the differences between the analysis and testing. [ABSTRACT FROM AUTHOR]

Snowfall affects the terrestrial climate system at high latitudes through its impacts on local meteorology, freshwater resources and energy balance. Precise snowfall monitoring is essential for cold countries such as Canada, and particularly in temperature-sensitive regions such as the Arctic; however, its size and remote location means the precipitation gauge network there is sparse. While satellite remote sensing of snowfall from instruments such as CloudSat-CPR offers a potential solution, satellite detection of precipitation phase has not been systematically evaluated across Canada. In this study, CloudSat-based precipitation occurrence and phase retrievals were validated at 26 stations across Canada maintained by Environment and Climate Change Canada (ECCC). Probability of Detection (POD), defined as the percentage agreement between coincident CloudSat and human-observed present weather information for precipitation (solid, liquid or no precipitation), and False Alarm Ratio (FAR) were used as the primary metrics for validation. The mean POD (FAR) for precipitation occurrence across Canada is 65.5% ± 4.3 (31.4% ± 5.1) and for no precipitation is 90.6% ± 1.4 (11% ± 2.5). The results show lower rates of detection under cloudier skies, in the presence of (freezing) drizzle and for lighter snowfall, which may be explained by a large number of false-positives due to CloudSat-CPR's high instrumental sensitivity. When CloudSat correctly detects the occurrence of precipitation, it shows uniformly high POD (>80%) and low FAR (<10%) for classifying the phase of precipitation. Large databases of coincident ground and satellite measurements allow us to provide a new estimate of around 9% for the frequency of virga events, a factor of two smaller than a previous estimate for the Arctic. The results from this study show that CloudSat has useful accuracy in detecting precipitation occurrence and very high accuracy at classifying precipitation phase, over diverse climate zones across Canada. As such, there is significant potential for satellite monitoring of snowfall in remote, cold regions. [ABSTRACT FROM AUTHOR]