Microwave Scanning Radiometer Research Articles

Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice

Sea ice and its surface snow are crucial components of the energy cycle and mass balance between the atmosphere and ocean, serving as sensitive indicators of climate change. Observing and understanding changes in snow depth on Antarctic sea ice are essential for sea ice research and global climate change studies. This study explores the feasibility of retrieving snow depth on Antarctic sea ice using data from the Chinese marine satellite HY-2B. Using generic retrieval algorithms, snow depth on Antarctic sea ice was retrieved from HY-2B Scanning Microwave Radiometer (SMR) data, and compared with existing snow depth products derived from other microwave radiometer data. A comparison against ship-based snow depth measurements from the Chinese 35th Antarctic Scientific Expedition shows that snow depth derived from HY-2B SMR data using the Comiso03 retrieval algorithm exhibits the lowest RMSD, with a deviation of −1.9 cm compared to the Markus98 and Shen22 models. The snow depth derived using the Comiso03 model from HY-2B SMR shows agreement with the GCOM-W1 AMSR-2 snow depth product released by the National Snow and Ice Data Center (NSIDC). Differences between the two primarily occur during the sea ice ablation and in the Bellingshausen Sea, Amundsen Sea, and the southern Pacific Ocean. In 2019, the monthly average snow depth on Antarctic sea ice reached its maximum in January (36.2 cm) and decreased to its minimum in May (15.3 cm). Thicker snow cover was observed in the Weddell Sea, Ross Sea, and Bellingshausen and Amundsen seas, primarily due to the presence of multi-year ice, while thinner snow cover was found in the southern Indian Ocean and the southern Pacific Ocean. The derived snow depth product from HY-2B SMR data demonstrates high accuracy in retrieving snow depth on Antarctic sea ice, highlighting its potential as a reliable alternative for snow depth measurements. This product significantly contributes to observing and understanding changes in snow depth on Antarctic sea ice and its relationship with climate change.

Polar Sea Ice Monitoring Using HY-2B Satellite Scatterometer and Scanning Microwave Radiometer Measurements

The Ku band microwave scatterometer (SCA) and scanning microwave radiometer (SMR) onboard HaiYang-2B (HY-2B) can simultaneously supply active and passive microwave observations over the polar region. In this paper, a polar ice water discrimination model and Arctic sea-ice-type classification model based on the support vector machine (SVM) method were established and used to produce a daily sea ice extent dataset from 2019 to 2021 with data from SCA and SMR. First, suitable scattering and radiation parameters are chosen as input data for the discriminant model. Then, the sea ice extent was obtained based on the monthly ice water discrimination model, and finally, the ice over the Arctic was classified into multiyear ice (MYI) and first-year ice (FYI). The 3-year ice extent and MYI extent products were consistent with the similar results of the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSISAF). Using the OSISAF similar product as validation data, the overall accuracies (OAs) of ice/water discrimination and FYI/MYI discrimination are 99% and 97%, respectively. Compared with the high spatial resolution classification results of the Moderate Resolution Imaging Spectroradiometer (MODIS) and SAR, the OAs of ice/water discrimination and FYI/MYI discrimination are 96% and 86%, respectively. In conclusion, the SAC and SMR of HY-2B have been verified for monitoring polar sea ice, and the sea ice extent and sea-ice-type products are promising for integration into long-term sea ice records.

Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972–1977

Abstract. The Electrically Scanning Microwave Radiometer (ESMR) instrument onboard the NIMBUS 5 satellite was a one-channel microwave radiometer that measured the 19.35 GHz horizontally polarized brightness temperature (TB) from 11 December 1972 to 16 May 1977. The original tape archive data in swath projection have recently been made available online by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Even though the ESMR was a predecessor of modern multi-frequency radiometers, there are still parts of modern processing methodologies which can be applied to the data to derive the sea ice extent globally. Here, we have reprocessed the entire dataset using a modern processing methodology that includes the implementation of pre-processing filtering, dynamical tie points, and a radiative transfer model (RTM) together with numerical weather prediction (NWP) for atmospheric correction. We present the one-channel sea ice concentration (SIC) algorithm and the model for computing temporally and spatially varying SIC uncertainty estimates. Post-processing steps include resampling to daily grids, land-spillover correction, the application of climatological masks, the setting of processing flags, and the estimation of sea ice extent, monthly means, and trends. This sea ice dataset derived from the NIMBUS 5 ESMR extends the sea ice record with an important reference from the mid-1970s. To make it easier to perform a consistent analysis of sea ice development over time, the same grid and land mask as used for EUMETSAT's OSI-SAF SMMR-based sea-ice climate data record (CDR) were used for our ESMR dataset. SIC uncertainties were included to further ease comparison to other datasets and time periods. We find that our sea ice extent in the Arctic and Antarctic in the 1970s is generally higher than those available from the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC), which were derived from the same ESMR dataset, with mean differences of 240 000 and 590 000 km2, respectively. When comparing monthly sea ice extents, the largest differences reach up to 2 million km2. Such large differences cannot be explained by the different grids and land masks of the datasets alone and must therefore also result from the differences in data filtering and algorithms, such as the dynamical tie points and atmospheric correction. The new ESMR SIC dataset has been released as part of the ESA Climate Change Initiative (ESA CCI) program and is publicly available at https://doi.org/10.5285/34a15b96f1134d9e95b9e486d74e49cf (Tonboe et al., 2023).

Measurement uncertainties of scanning microwave radiometers and their influence on temperature profiling

Abstract. In order to improve observations of the atmospheric boundary layer (ABL), the European Meteorological Network, EUMETNET, and the Aerosol, Clouds, and Trace Gases Research Infrastructure, ACTRIS, are currently working on building networks of microwave radiometers (MWRs). Elevation-scanning MWRs are well suited to obtain temperature profiles of the atmosphere, especially within the ABL. Understanding and assessing measurement uncertainties of state-of-the-art scanning MWRs is therefore crucial for accurate temperature profiling. In this paper, we discuss measurement uncertainties due to the instrument setup and originating from external sources, namely (1) horizontal inhomogeneities of the atmosphere, (2) pointing errors or a tilt of the instrument, (3) physical obstacles in the line of sight of the instrument, and (4) radio frequency interference (RFI). Horizontal inhomogeneities from observations at the Jülich Observatory for Cloud Evolution (JOYCE) are shown to have a small impact on retrieved temperature profiles (<|0.22K| in the 25th and 75th percentiles below 3000 m). Typical instrument tilts, that could be caused by uncertainties during the instrument setup, also have a very small impact on temperature profiles and are smaller than 0.1 K below 3000 m for up to 1∘ of tilt. Physical obstacles at ambient temperatures and in the line of sight and filling the complete beam of the MWR at the lowest elevation angle of 5.4∘ have to be at least 600 m away from the instrument in order to have an impact of less than 0.1 K on obtained temperature profiles. If the obstacle is 5 K warmer than its surroundings then the obstacle should be at least 2700 m away. Finally, we present an approach on how to detect RFI with an MWR with azimuth and elevation-scanning capabilities. In this study, we detect RFIs in a water vapor channel that does not influence temperature retrievals but would be relevant if the MWR were used to detect horizontal humidity inhomogeneities.

Impact of HY‐2B SMR radiance assimilation on CMA global medium‐range weather forecasts

AbstractThe Chinese second ocean dynamic environment satellite Haiyang‐2B (HY‐2B) was successfully launched on October 25, 2018, carrying a scanning microwave radiometer (SMR) to provide information about the ocean and atmosphere. For the first time, this study investigated the impact of radiance data from the HY‐2B SMR in the four‐dimensional variational (4DVar) data assimilation system of the Global Forecast System developed by the China Meteorology Administration (CMA‐GFS). Prior to the radiance assimilation, we evaluated the data quality and developed suitable quality control (QC) procedures for the HY‐2B SMR observations. In addition, the cloud liquid water path (CLWP) and ocean wind speed (OWS) were introduced as new bias correction (BC) predictors to remove the systematic bias originating from the forward operator in the CMA‐GFS 4DVar system. The assimilation effects of SMR observations were evaluated through one‐month cycling experiments. Verification with independent observations and reanalysis data has demonstrated that assimilating SMR clear‐sky radiance can greatly reduce the analysis errors for temperature and humidity, while indirectly improving the quality of wind analysis. Furthermore, significant improvements in geopotential height forecasts were found for days 1–5 in the lower troposphere of the tropical region.

An Improved Resampling Method for Brightness Temperature of HY-2B Scanning Microwave Radiometer

An Improved Resampling Method for Brightness Temperature of HY-2B Scanning Microwave Radiometer

Sea ice concentration inversion based on ASI algorithm combined with Bootstrap algorithm

Weather filters are frequently used to lessen the interference while using the ARTSIST Sea Ice (ASI) algorithm, which is based on Advanced Microwave Scanning Radiometer for EOS/Advanced Microwave Scanning Radiometer-2 (AMSR-E/AMSR-2) 89 GHz brightness temperature (Tb) data to calculate sea ice concentration (SIC) with high spatial resolution. However, the weather filters may cause low concentration sea ice to be mistaken for sea water, which decreases the accuracy of the SIC inversion. This paper proposed the ASI algorithm combined with the Bootstrap (BT) algorithm for SIC inversion to increase the accuracy of the SIC inversion. First, use the weather filters to screen out an area that has been disrupted by the external factors like clouds and water vapor. Second, the BT algorithm is used in this area to accomplish SIC inversion. Based on the proposed method, the Antarctic SIC results in February 2018 were obtained and compared with the SIC obtained from the ASI algorithm. The results demonstrated that the SIC inversion based on ASI algorithm combined with BT algorithm is practicable. By comparing the SIC results of Landsat-8 data, it was further verified that the SIC inversion based on ASI algorithm combined with BT algorithm has higher accuracy compared to the ASI algorithm. As a result, the accuracy of SIC inversion can be improved to some extent by combining the ASI algorithm and the BT algorithm.

Global statistical assessment of Haiyang-2B scanning microwave radiometer precipitable water vapor

Introduction: As a successor to Haiyang-2A (HY-2A), HY-2B is China’s second marine dynamic satellite. Equipped with a scanning microwave radiometer (SMR), it can measure the precipitable water vapor (PWV) over the oceans, providing valuable climate and weather insights. This study aims to evaluate the accuracy of HY-2B SMR PWV data from January 2019 to December 2021 using various validation methods.Methods: to validate HY-2B SMR PWV, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data, radiosonde data, and global navigation satellite system (GNSS) observations are used.Results: the validation shows that the HY-2B SMR PWV data agree well with the ERA5 PWV data, with a root mean square error (RMSE) of 1.61 mm and a mean value of 0.71 mm. However, RMSEs of approximately 3.5 mm are observed when comparing the HY-2B SMR PWV data to GNSS and radiosonde data, likely due to temporal and spatial gaps. Geographically, there are greater differences between the HY-2B SMR and ERA5 in lower-latitude areas. The reason could be attributed to a higher water vapor content and more frequent cloudy weather in the areas.Discussion: The results demonstrate that the HY-2B SMR PWV meets requirement (RMSE≤3.5 mm). The HY-2B SMR PWV has a high quality, with a slight observational drift of only 0.1 mm/year, but regular validation and calibration are still necessary.

Biases’ Characteristics Assessment of the HY-2B Scanning Microwave Radiometer (SMR)’s Observations

The second Chinese ocean dynamic environment satellite Haiyang-2B (HY-2B), carrying a scanning microwave radiometer (SMR) to provide information on the ocean and atmosphere, was successfully launched on 25 October 2018. Before the data assimilation, it is necessary to characterize and evaluate the biases of the HY-2B SMR observations. This study is the first to conduct a systematic assessment of the SMR radiance data based on observation minus background simulation (O-B). Three types of numerical weather prediction (NWP) datasets, including ECMWF Reanalysis v5 (ERA5), the analysis fields from the NCEP Global Forecast System (NCEP-GFS), and the analysis fields from the Global Regional Assimilation and Prediction System-Global Forecast System (GRAPES-GFS), were used as input information for RTTOV v12.3 to simulate the SMR’s observed brightness temperature (TB) under clear-sky conditions. Study results showed that the O-B biases and IQR of the SMR for most channels were within −2.5–0.4 K and smaller than 4 K, respectively. The SMR observations were generally consistent with the RTTOV simulations, even based on the different NWP fields. These results indicate a good prospect for the assimilated application of HY-2B SMR radiance data. However, due to the impact of RFI, the SMR’s descending data for two 10.7 GHz channels showed some significant positive biases larger than 50 K over the seas of the European region. In addition, it seems that the bias characteristics of the SMR’s ascending data were obviously different from those of the descending data. It was also found that the variation trend of scan-position-dependent bias was generally stable for the SMR’s ascending data but fluctuates significantly for the descending data, with a maximum amplitude greater than 0.7 K for some channels.

Spatial Resolution Enhancement of HY-2B Scanning Microwave Radiometer Low-Frequency Data

Spatial Resolution Enhancement of HY-2B Scanning Microwave Radiometer Low-Frequency Data

Instrument Design and Early In-Orbit Performance of HY-2B Scanning Microwave Radiometer

Currently, many microwave radiometers have been successfully used in earth-observation and ocean-monitoring systems. In China, the scanning microwave radiometers (SMR) onboard the Haiyang-2A/2B (HY-2A/2B) satellites are multichannel radiometers capable of obtaining oceanic and atmospheric parameters, such as sea surface temperature (SST), sea surface wind speed (WS), water vapor (WV), and cloud liquid water (LW). As a successor to HY-2A, the HY-2B satellite was launched successfully on October 25, 2018. Compared with the HY-2A’s SMR, the HY-2B’s SMR is an improved microwave radiometer. This article is concerned with the instrument design of the HY-2B’s SMR and its improvements. The important improvements of the HY-2B’s SMR in regard to the feedhorns and cold-sky reflector (CSR) are verified, and the early in-orbit performance of the HY-2B’s SMR is evaluated. The results demonstrate that the HY-2B’s SMR exhibits an important improvement on the HY-2A’s SMR in terms of the quality of the brightness temperature (TB) products and calibration accuracy. The results also show that the HY-2B’s SMR has a good performance in terms of the global TB images and the retrieved products with a good agreement with the expectations.

Stability of the HY-2B Scanning Microwave Radiometer (SMR) Brightness Temperature Using a Modified Vicarious Cold Reference

Long-term and stable brightness temperature (TB) measurements observed by microwave radiometers are of great significance in studying the variation in geophysical parameters and their trend analysis. A modified vicarious cold reference (MVCR) method for the scanning microwave radiometer (SMR) TBs onboard the Haiyang-2B (HY-2B) satellite is developed and used to estimate the stability of the TBs of L2A-TB and L2A-TC, which corresponds to TBs before and after intercalibration, respectively. Data from November 1, 2018, to October 31, 2021, are used for the stability assessment. The results show that there are several channels in L2A-TB with annual drifts of TBs greater than 0.1 K per year (K/year), while the TBs of L2A-TC after intercalibration are quite stable, with annual drifts that are all less than 0.1 K/year. In conclusion, the SMR TBs after intercalibration achieve better stability. The robustness of the MVCR method is demonstrated using simulated SMR TBs. After removing the annual harmonics, the coldest TB sequence is more stable, and a more realistic annual drift can be obtained.

Estimation of HY-2B Main Reflector Emissivity From Cold Space Observations

The scanning microwave radiometer (SMR) is an important payload of the Haiyang-2B (HY-2B) satellite. The main reflector of the SMR has its own emission due to the imperfect reflector material and surface roughness; however, the reflector’s emissivity was not measured or estimated during the prelaunch phase. During the post-launch phase, it was discovered that the radiation contribution from the reflector changed with its position and over time. This phenomenon affected the accuracy of the calibration by a few Kelvins. This report analyzes the main reflector’s emissivity based on the observed results when HY-2B pitched over to view the homogeneous and isotropic cold space. The emissivity of the reflector was approximately 3%, but owing to the larger field of view, the emissivity of 6.925 and 10.7 GHz were not well assessed. After the radiation from the reflector has been corrected, the calibration error caused by the reflector’s emissivity is reduced.

An Enhanced Resolution Brightness Temperature Product for Future Conical Scanning Microwave Radiometers

An enhanced spatial resolution brightness temperature product is proposed for future conical scan microwave radiometers. The technique is developed for Copernicus Imaging Microwave Radiometer (CIMR) measurements that are simulated using the CIMR antenna pattern at the L-band and the measurement geometry proposed in the Phase A study led by Airbus. An inverse antenna pattern reconstruction method is proposed. Reconstructions are obtained using two CIMR configurations, namely, using measurements collected at L-band by the forward (FWD) scans only, and combining forward and backward (FWD+BWD) scans. Two spatial grids are adopted, namely, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3 \mathrm {\,\,km} \times 3 \mathrm {\,\,km}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$36 \mathrm {\,\,km} \times 36 \mathrm {\,\,km}$ </tex-math></inline-formula> . Simulation results, referred to synthetic and realistic reference brightness fields, demonstrate the soundness of the proposed scheme that provides brightness temperature fields reconstructed at a spatial resolution up to ~1.9 times finer than the measured field when using the FWD+BWD combination.

Ocean and Sea Ice Retrievals From an End‐To‐End Simulation of the Copernicus Imaging Microwave Radiometer (CIMR) 1.4–36.5 GHz Measurements

AbstractThe Copernicus Imaging Microwave Radiometer (CIMR) is currently being implemented by the European Space Agency (ESA) as a Copernicus Expansion Mission primarily designed to observe the Polar Regions in support of the Integrated European Policy for the Arctic. It is a conically scanning microwave radiometer with polarized channels centered at 1.414, 6.925, 10.65, 18.7, and 36.5 GHz and channel NEΔT between 0.2 and 0.7 K. A large rotating deployable mesh reflector will provide real‐aperture resolutions ranging from 60 (1.4 GHz) to 5 km (36.5 GHz). To evaluate CIMR retrieval performance, a simplified end‐to‐end simulation of the mission has been carried out. The simulation includes important processes and input parameters, such as test geophysical datasets, forward models, an instrument simulator, and retrieval algorithms to derive the key mission geophysical products. The forward modeling is tested by producing Brightness Temperatures (TBs) from 4 global scenes. A comparison with current observations of the open ocean and sea ice at similar frequencies confirmed the realism of the simulations. The produced top‐of‐atmosphere TBs are converted to Antenna brightness Temperatures (TAs), taking into account the instrument design, and are then inverted to retrieve Sea Ice Concentration (SIC), Sea Surface Temperature (SST), and Sea Surface Salinity (SSS). Evaluating the retrieval performance showed that the simulated CIMR instrument can provide SST, SSS, and SIC measurements with precisions and spatial resolutions conforming with the mission requirements. The evaluation also highlighted the challenges of observing the Arctic environment and put in perspective CIMR capabilities compared with current instruments.

Investigation of the Mixing Height in the Planetary Boundary Layer by Using Sodar and Microwave Radiometer Data

The height of the mixing layer is a significant parameter for describing the dynamics of the planetary boundary layer (PBL), especially for air quality control and for the parametrizations in numerical modeling. The problem is that the heights of the mixing layer cannot be measured directly. The values of this parameter are depending both on the applied algorithms for calculation and on the measuring instruments which have been used by the data source. To determine the height of a layer of intense turbulent heat exchange, data were used from acoustic meteorological locator (sodar) and from a passive single-channel scanning microwave radiometer MTP-5 (MWR) to measure the temperature profile in a layer of up to 1 km. Sodar can provide information on the structure of temperature turbulence in the PBL directly. These data have been compared with the mixing layer height calculated with the Parcel method by using the MTP-5 data. For the analysis, July and September 2020 were selected in the city of Tomsk in Siberia as characteristic periods of mid-summer and the transition period to autumn. The measurement results, calculations and inter-comparisons are shown and discussed in this work. During temperature inversions in the boundary layer, it was observed that turbulent heat transfer (increased dispersion of air temperature) is covering the inversion layers and the overlying ones. Moreover, this phenomenon is not only occurring during the morning destruction of inversions, but also in the process of their formation and development.

Classification of the frozen/thawed surface state of Northern land areas based on SMAP and GCOM-W1 brightness temperature observations at 1.4 GHz and 6.9 GHz

ABSTRACT In this letter, the method created earlier by the authors and the information product SPL3FTP_E of the Soil Moisture Active Passive (SMAP) satellite for determining frozen/thawed state of soil surface on the example of test sites placed on North Slope of Alaska, U.S.A., Canada, Finland and Russian Federation were compared. As an indicator of the frozen/thawed state of soil surface, the polarization index calculated on the basis of the reflectivity of soils was proposed. The soil reflectivity was determined in the L-band based on the SMAP radiometric observations at a frequency of 1.4 GHz using the values of brightness temperatures measured by the Global Change Observation Mission – Water 1/Advanced Microwave Scanning Radiometer 2 (GCOM-W1/AMSR2) at a vertical polarization and a frequency of 6.9 GHz, as an estimate of the soil effective temperature. As a result, it was shown that the developed method makes it possible to increase accuracy of the frozen/thawed states determination of soil surface from 3% to 9% in relation to the SMAP data (SPL3FTP_E) for twelve Arctic test sites.

A Rapid Beam Pointing Determination and Beam-Pointing Error Analysis Method for a Geostationary Orbiting Microwave Radiometer Antenna in Consideration of Antenna Thermal Distortions.

When observing the Earth’s radiation signal with a geostationary orbiting (GEO) mechanically scanned microwave radiometer, it is necessary to correct the antenna beam pointing (ABP) in real time for the deviation caused by thermal distortions of antenna reflectors with the help of the on-board Image Navigation and Registration (INR) system during scanning of the Earth. The traditional ABP determination and beam-pointing error (BPE) analysis method is based on the electromechanical coupling principle, which usurps time and computing resources and thus cannot meet the requirement for frequent real-time on-board INR operations needed by the GEO microwave radiometer. For this reason, matrix optics (MO), which is widely used in characterizing the optical path of the visible/infrared sensor, is extended to this study so that it can be applied to model the equivalent optical path of the microwave antenna with a much more complicated configuration. Based on the extended MO method, the ideal ABP determination model and the model for determining the actual ABP affected by reflector thermal distortions are deduced for China’s future GEO radiometer, and an MO-based BPE computing method, which establishes a direct connection between the reflector thermal distortion errors (TDEs) and the thermally induced BPE, is defined. To verify the overall performance of the extended MO method for rapid ABP determination, the outputs from the ideal ABP determination model were compared to calculations from GRASP 10.3 software. The experimental results show that the MO-based ABP determination model can achieve the same results as GRASP software with a significant advantage in computational efficiency (e.g., at the lowest frequency band of 54 GHz, our MO-based model yielded a 4,730,000 times faster computation time than the GRASP software). After validating the correctness of the extended MO method, the impacts of the reflector TDEs on the BPE were quantified on a case-by-case basis with the help of the defined BPE computing method, and those TDEs that had a significant impact on the BPE were therefore identified. The methods and results presented in this study are expected to set the basis for the further development of on-board INR systems to be used in China’s future GEO microwave radiometer and benefit the ABP determination and BEP analysis of other antenna configurations to a certain extent.

The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

Contribution of Snow-Melt Water to the Streamflow over the Three-River Headwater Region, China

Snowmelt water is essential to the water resources management over the Three-River Headwater Region (TRHR), where hydrological processes are influenced by snowmelt runoff and sensitive to climate change. The objectives of this study were to analyse the contribution of snowmelt water to the total streamflow (fQ,snow) in the TRHR by applying a snowmelt tracking algorithm and Variable Infiltration Capacity (VIC) model. The ratio of snowfall to precipitation, and the variation of the April 1 snow water equivalent (SWE) associated with fQ,snow, were identified to analyse the role of snowpack in the hydrological cycle. Prior to the simulation, the VIC model was validated based on the observed streamflow data to recognize its adequacy in the region. In order to improve the VIC model in snow hydrology simulation, Advanced Scanning Microwave Radiometer E (ASMR-E) SWE product data was used to compare with VIC output SWE to adjust the snow parameters. From 1971 to 2007, the averaged fQ,snow was 19.9% with a significant decreasing trend over entire TRHR (p &lt; 0.05).The influence factor resulted in the rate of change in fQ,snow which were different for each sub-basin TRHR. The decreasing rate of fQ,snow was highest of 0.24%/year for S_Lantsang, which should be due to the increasing streamflow and the decreasing snowmelt water. For the S_Yangtze, the increasing streamflow contributed more than the stable change of snowmelt water to the decreasing fQ,snow with a rate of 0.1%/year. The April 1 SWE with the minimum value appearing after 2000 and the decreased ratio of snowfall to precipitation during the study period, suggested the snow solid water resource over the TRHR was shrinking. Our results imply that the role of snow in the snow-hydrological regime is weakening in the TRHR in terms of water supplement and runoff regulation due to the decreased fQ,snow and snowfall.