Microwave Imager Data Research Articles

Testing a spaceborne passive‐microwave severe hail retrieval over Argentina using ground‐based dual‐polarization radar data

AbstractPassive‐microwave hail detection methods rely on the scattering signal produced by large ice hydrometeors. Recently, passive‐microwave hail retrieval methods have been trained by pairing Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Global Precipitation Measurement (GPM) Microwave Imager (GMI) data with United States surface hail reports to present a global hail climatology. In particular, Southeastern South America (SESA) stands out as a hotspot for hail occurrence, more specifically northeastern Argentina. However, the analysis performed with the available ground hail reports in Argentina shows some differences from the available ground hail reports in Argentina, as it locates the most frequent hail storms in the southwestern (Mendoza and Córdoba) and northeastern (Misiones) regions of SESA. To assess the retrieval from a regional perspective, we take a case study approach and focus on polarization‐corrected temperature features (PCTFs) with a detected . We mainly rely on polarimetric weather radars and a hydrometeor identification algorithm (HID) as a proxy for ground truth. Results indicate that the hail retrieval responds mainly to low 37‐GHz polarized corrected (brightness) temperatures (PCTs) and large PCTF area, while HID outputs show that high probabilities of hail also correspond to abundantly suspended graupel. The observed passive‐microwave hail detection hotspot could be influenced by mesoscale convective systems with deep columns of graupel‐sized frozen hydrometeors, common in northeastern Argentina.

Hydrometeor Identification Using GMI Passive Microwave Brightness Temperatures

Abstract Using passive microwave brightness temperatures Tb from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and hydrometeor identification (HID) data from dual-polarization ground radars, empirical lookup tables are developed for a multifrequency estimation of the likelihood a precipitation column includes certain hydrometeor types, as a function of Tb. Eight years of collocated Tb and HID data from the GPM Validation Network are used for development and testing of the GMI-based HID retrieval, with 2015–20 used for training and 2021–22 used for testing the GMI-based HID retrieval. The occurrence of profiles with hail and graupel are both slightly underpredicted by the lookup tables, but the percentage of profiles predicted is highly correlated with the percentage observed (0.98 correlation coefficient for hail and 0.99 for graupel). By having snow appear before rain in the hierarchy, the sample size for rain, without ice aloft, is fairly small, and the percentage of rain profiles is less than snow for all Tb.

Uncertainty Calibration of Passive Microwave Brightness Temperatures Predicted by Bayesian Deep Learning Models

Abstract Visible and infrared radiance products of geostationary orbiting platforms provide virtually continuous observations of Earth. In contrast, low-Earth orbiters observe passive microwave (PMW) radiances at any location much less frequently. Prior literature demonstrates the ability of a machine learning (ML) approach to build a link between these two complementary radiance spectra by predicting PMW observations using infrared and visible products collected from geostationary instruments, which could potentially deliver a highly desirable synthetic PMW product with nearly continuous spatiotemporal coverage. However, current ML models lack the ability to provide a measure of uncertainty of such a product, significantly limiting its applications. In this work, Bayesian deep learning is employed to generate synthetic Global Precipitation Measurement (GPM) Microwave Imager (GMI) data from Advanced Baseline Imager (ABI) observations with attached uncertainties over the ocean. The study first uses deterministic residual networks (ResNets) to generate synthetic GMI brightness temperatures with as little mean absolute error as 1.72 K at the ABI spatiotemporal resolution. Then, for the same task, we use three Bayesian ResNet models to produce a comparable amount of error while providing previously unavailable predictive variance (i.e., uncertainty) for each synthetic data point. We find that the Flipout configuration provides the most robust calibration between uncertainty and error across GMI frequencies, and then demonstrate how this additional information is useful for discarding high-error synthetic data points prior to use by downstream applications.

A Statistical-Based Model for Typhoon Rain Hazard Assessment

Extreme typhoon rainfall can lead to damaging floods near the coastal region in mainland China. In the present study, we calibrate the parameters for a parametric hurricane rain model by using the precipitation radar (PR) data from the Tropical Rainfall Measuring Mission (TRMM) (i.e., PR-TRMM) and the TRMM microwave imager (TMI) data (i.e., TMI-TRMM). To show the applicability of the model for the tropical cyclone (TC) rain hazard assessment, we combine the developed rainfall intensity model with historical and synthetic TC tracks to estimate the T-year return period value of the accumulated rainfall in 24 h, QA24-T. We map QA24-100 for part of the coastal region in mainland China, showing that the spatial variation of QA24-100 is relatively smooth. It was found that the estimated QA24-100 using the model developed, based on the snapshots from PR-TRMM, is about 60% of that obtained using the model developed based on the snapshots from TMI-TRMM. This reflects the differences in the rainfall intensities reported in TMI-TRMM and PR-TRMM. As part of verification, we compare the estimated return period value to that obtained by using the record from surface meteorological stations at a few locations. The comparison indicates that, on average, QA24-100 based on gauge data is about 1.4 and 2.3 times that obtained using the model developed based on the snapshots from PR-TRMM and TRM-TRMM, respectively. This suggests that, for TC rain hazard estimation, one may consider the empirical scaling factor of 1.4 and 2.4 for the rainfall intensity models developed based on snapshots from PR-TRMM and TMI-TRMM, respectively.

On the Trade-Off Between Enhancement of the Spatial Resolution and Noise Amplification in Conical-Scanning Microwave Radiometers

The ability to enhance the spatial resolution of measurements collected by a conical-scanning microwave radiometer (MWR) is discussed in terms of noise amplification and improvement of the spatial resolution. Simulated (and actual) brightness temperature profiles are analyzed at variance of different intrinsic spatial resolutions and adjacent beams overlapping modeling a simplified 1-D measurement configuration (MC). The actual measurements refer to Special Sensor Microwave Imager (SSM/I) data collected using the 19.35 and the 37.00 GHz channels that match the simulated configurations. The reconstruction of the brightness profile at enhanced spatial resolution is performed using an iterative gradient method which allows a fine tuning of the level of regularization. Objective metrics are introduced to quantify the enhancement of the spatial resolution and noise amplification. Numerical experiments, performed using the simplified 1-D MC, show that the regularized deconvolution results in negligible advantages when dealing with low-overlapping/fine-spatial-resolution configurations. Regularization is a mandatory step when addressing the high-overlapping/low-spatial-resolution case and the spatial resolution can be enhanced up to 2.34 with a noise amplification equal to 1.56. A more stringent requirement on the noise amplification (up to 0.6) results in an improvement of the spatial resolution up to 1.64.

The Challenge of Surface Type Changes Over the Aral Sea for Satellite Remote Sensing of Precipitation

Frequent false signals of precipitation in satellite passive microwave retrievals over the Aral Sea have been identified as being caused by an outdated surface database. The database includes the surface type, elevation, and the percentage of the primary surface type in each grid. It was also found that the grid resolution of 1/6 degree (∼18 km at the equator) of the outdated surface data was too coarse to process Global Precipitation Measurement (GPM) Mission microwave (GMI) imager data, which have a field of view (FOV) resolution of 31.68 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for channels at 89 GHz, 166.5 GHz and 183.31 GHz). In this study, we generated a new surface database at a resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.05^\circ \times 0.05^\circ $</tex-math></inline-formula> (∼5.6 km at the equator). Using the new surface data, the false precipitation problem above is addressed. At the same time, the retrieval accuracy for other parameters such as total precipitable water over the Aral Sea is significantly improved as well. The resolution of the database is suitable for most microwave and infrared sounding measurements of atmospheric temperature and moisture profiles as well as precipitation. By comparing this new surface data against the outdated surface data, we also see the loss of permanent ice in Antarctica and a dramatic reduction of water surface over the Aral Sea. Using a 40-year record of remote sensing data, we can observe the steady decrease in size of the Aral Sea, as a result of regional water use policies and natural climate change.

Railway obstacle detection based on radar and image data fusion

In order to improve the poor adaptability of a single sensor in detection and further improve the train’s ability to perceive the running environment ahead, a railway obstacle detection method based on the fusion of microwave radar and image data was proposed. The environment in front of the train collected by the radar is preprocessed to eliminate false and out-of-bounds target signals, and the target within the safety limit is retained. At the same time, the frame difference method is used to detect the obstacles in the image sequence captured by the vehicle camera. The detection results of radar and camera are fused at the decision level. The experimental results show that this method can effectively realize the joint detection of obstacles by microwave radar and machine vision, which makes up for the deficiency of single sensor in obstacle detection.

Rainfall rate estimation over India using global precipitation measurement’s microwave imager datasets and different variants of fuzzy information system

Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithms, rain rate estimations are continuously improving. This study provides a comparative performance appraisal of three hybrid machine learning algorithms namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) and Hybrid Fuzzy Inference System (HYFIS) for rain rate estimation using the Global Precipitation Measurement (GPM)’s Microwave Imager (GMI) and ground-based Disdrometer data. The in situ sampling was conducted at four different locations (both land and ocean) across the Indian region and different statistical metrics were used to evaluate the performances of these models. The results showed that HYFIS algorithm has provided better rain rate estimation than ANFIS and DENFIS. The study endorses these neuro-fuzzy models for generating accurate precipitation products and can be considered as an alternative for future satellite retrieval algorithms.

The Framework for Assimilating All-Sky GPM Microwave Imager Brightness Temperature Data in the NASA GEOS Data Assimilation System

Abstract Satellite radiance observations combine global coverage with high temporal and spatial resolution, and bring vital information to NWP analyses especially in areas where conventional data are sparse. However, most satellite observations that are actively assimilated have been limited to clear-sky conditions due to difficulties associated with accounting for non-Gaussian error characteristics, nonlinearity, and the development of appropriate observation operators for cloud- and precipitation-affected satellite radiance data. This article provides an overview of the development of the Gridpoint Statistical Interpolation (GSI) configurations to assimilate all-sky data from microwave imagers such as the GPM Microwave Imager (GMI) in the NASA Goddard Earth Observing System (GEOS). Electromagnetic characteristics associated with their wavelengths allow microwave imager data to be highly sensitive to precipitation. Therefore, all-sky data assimilation efforts described in this study are primarily focused on utilizing these data in precipitating regions. To utilize data in cloudy and precipitating regions, state and analysis variables have been added for ice cloud, liquid cloud, rain, and snow. This required enhancing the observation operator to simulate radiances in heavy precipitation, including frozen precipitation. Background error covariances in both the central analysis and EnKF analysis in the GEOS hybrid 4D-EnVar system have been expanded to include hydrometeors. In addition, the bias correction scheme was enhanced to reduce biases associated with thick clouds and precipitation. The results from single observation experiments demonstrate the capability of assimilating all-sky microwave brightness temperature data in GEOS both when the model forecast produces excessive precipitation and too little precipitation. Additional experiments show that hydrometeors and dynamic variables such as winds and pressure are adjusted in physically consistent ways in response to the assimilation.

Diurnal Cycles of Precipitation and Lightning in the Tropics Observed by TRMM3G68, GSMaP, LIS, and WWLLN

AbstractDiurnal cycles of precipitation and lightning are investigated by analyzing rain rates of the TRMM3G68 dataset, consisting of Precipitation Radar and Microwave Imager data only; rain rates of Global Satellite Mapping of Precipitation (GSMaP), for which infrared (IR) data are also used; lightning flash rates observed by TRMM Lightning Imaging Sensor (LIS); and lightning stroke rates of World Wide Lightning Location Network (WWLLN) over the tropics. Diurnal amplitudes relative to averages are generally larger for lightning than for precipitation. Over ocean, relative amplitudes are stronger in the stratocumulus deck region in the southeast Pacific than those over typical ocean regions. The phase of GSMaP is substantially delayed to TRMM3G68 due to the phase-delay problem of IR-based estimation. The diurnal peaks tend to occur between 1400 and 1800 LST over the continent after spatial averaging with a phase leading order of TRMM3G68, LIS, and WWLLN, and between 0000 and 0700 LST over oceanic regions where diurnal cycles are prominent in all datasets. Off-equatorward phase propagations are found in the precipitation in the Pacific and Indian Oceans. Over selected coastal regions, all data exhibit consistent oceanward phase propagation with the longest, medium, and shortest phase propagation distances for TRMM3G68 precipitation, WWLLN lightning, and LIS lightning, respectively, with a phase leading order of LIS, WWLLN, and TRMM3G68. The summertime diurnal cycle over the Gulf Stream also exhibits oceanward phase propagation, but with strong amplitude enhancement over the Gulf Stream. Diurnal cycle amplitude is also enhanced over the Kuroshio in the East China Sea in the baiu–mei-yu rainy season.

Ice Concentration Estimation Method with Satellite based Microwave Radiometer by Means of Inversion Theory

Ice concentration estimation method with satellite-based microwave radiometer by means of inversion theory is proposed. Through experiments, it is found that the proposed methods are superior to the existing methods, the NASA Team algorithm and the Comiso's Bootstrap algorithm with up to 45% of improvement on ice concentration estimation accuracy based on the simulation study. Also 1.5 to 2.1% of improvement was achieved for the proposed method compared to the NASA Team and Comiso's Bootstrap algorithms for the actual The Special Sensor Microwave Imager (SSM/I) data of Okhotsk using Japanese Earth Resources Satellite: JERS-1/Synthetic Aperture Radar: SAR data as a truth data for estimating ice concentration.

A New Restoration Method for Radio Frequency Interference Effects on AMSR-2 over North America

Observations from spaceborne microwave imagers are important sources of land surface information. However, the low-frequency channels of microwave imagers are easily interfered with by active radio signals with similar frequencies. Radio frequency interference (RFI) signals are widely distributed because of the lack of frequency protection, which seriously hinders the application of microwave imager data in data assimilation and retrieval research. In this paper, a new data restoration method is proposed based on principal component analysis (PCA). Both the ideal and real reconstruction experiments show that the new method can effectively repair abnormal observations interfered by RFI compared with the commonly used Cressman interpolation method because observation information over the whole selected domain is used for restoration in the new method, whereas Cressman interpolation uses only a selection of data around the target observation. The observation errors in the data with RFI can be reduced by one order of magnitude by means of the new method and little artificial information is introduced. One-week restoration validation also proves that the new method has a stable accuracy and broad application prospects.

Identification of Sun Glint Contamination in GMI Measurements Over the Global Ocean

This paper utilizes the model regression difference method to identify sun glint contamination on Global Precipitation Measurement Microwave Imager (GMI) data over the ocean based on observations from 2015 to 2016. The spatial distribution characteristics and the critical angles of the sun glint flags are analyzed in depth. It is found that the GMI measurements with horizontal and vertical polarizations at 10.65 GHz over the ocean are sometimes contaminated by the solar radiation reflected by the sea surface. The sun glint contamination has also been detected over high reflected land surface. The intensity and locations of the contamination are related to the sun glint angle. Only those GMI field of views with smaller sun glint angles are easily contaminated. The closer the sun glint angle is to 0°, the stronger the magnitude of the contamination. The GMI observations at other channels are not contaminated mainly because sun glint is most pronounced at 10 GHz. There are too strong constraints and tossing out of too many useful data in current GMI sun glint algorithms. The suggested critical angles of the sun glint flags for 10.65GHz is 20° to reduce false flagging. By applying the model regression difference method, the error in brightness temperature caused by sun glint can be corrected. The Tropical Rainfall Measuring Mission Microwave Imager (TMI) observations at 10.65 GHz are also contaminated by the reflected solar radiation from the ocean, and the intensity and locations of the contamination are similar to those of the GMI.

Tropical Cyclone Rain Retrievals from FY-3B MWRI Brightness Temperatures Using the Goddard Profiling Algorithm (GPROF)

AbstractThis study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.

All-Sky Radiance Assimilation of ATMS in HWRF: A Demonstration Study

Abstract Satellite all-sky radiances from the Advanced Technology Microwave Sounder (ATMS) are assimilated into the Hurricane Weather Research and Forecasting (HWRF) Model using the hybrid Gridpoint Statistical Interpolation analysis system (GSI). To extend the all-sky capability recently developed for global applications to HWRF, some modifications in HWRF and GSI are facilitated. In particular, total condensate is added as a control variable, and six distinct hydrometeor habits are added as state variables in hybrid GSI within HWRF. That is, clear-sky together with cloudy and precipitation-affected satellite pixels are assimilated using the Community Radiative Transfer Model (CRTM) as a forward operator that includes hydrometeor information and Jacobians with respect to hydrometeor variables. A single case study with the 2014 Atlantic storm Hurricane Cristobal is used to demonstrate the methodology of extending the global all-sky capability to HWRF due to ATMS data availability. Two data assimilation experiments are carried out. One experiment uses the operational configuration and assimilates ATMS radiances under the clear-sky condition, and the other experiment uses the modified HWRF system and assimilates ATMS radiances under the all-sky condition with the inclusion of total condensate update and cycling. Observed and synthetic Geostationary Operational Environmental Satellite (GOES)-13 data along with Global Precipitation Measurement Mission (GPM) Microwave Imager (GMI) data from the two experiments are used to show that the experiment with all-sky ATMS radiances assimilation has cloud signatures that are supported by observations. In contrast, there is lack of clouds in the initial state that led to a noticeable lag of cloud development in the experiment that assimilates clear-sky radiances.

Diurnal Variation of Liquid Water Path Derived From Two Polar‐Orbiting FengYun‐3 MicroWave Radiation Imagers

AbstractMicroWave Radiation Imagers (MWRIs) onboard the afternoon FengYun‐3B (FY‐3B) and midmorning FengYun‐3C (FY‐3C) satellites provide global observations almost 4 times a day at any location. This study examines the diurnal cycle of the liquid water path (LWP) over the southeast Pacific Ocean using MWRI data. A double‐difference calibration method is first used to evaluate the MWRI calibration bias based on Tropical Rainfall Measuring Mission Microwave Imager (TMI) measurements. LWP diurnal cycle characteristics are derived from FY‐3B/C MWRI data measured in 2014. The diurnal variation of LWP from combined FY‐3B/C MWRI measurements are consistent with that derived from Tropical Rainfall Measuring Mission Microwave Imager data. Maximum and minimum LWPs occurred in the early morning and afternoon, respectively. The largest diurnal amplitude (~40% of the mean LWP) was located near 85°W/20°S. The diurnal amplitude varied from season to season. MWRI data can provide important constraints for models simulating the cloud diurnal cycle.

Identifying Desert Regions for Improved Intercalibration of Satellite Microwave Radiometers

This paper describes the technical approach to generate homogeneous warm brightness temperature scene binary mask over the earth for the intercalibration of microwave imagers. The objective is to identify homogeneous desert scenes as targets in radiometric intercalibration of multisource multisensor systems such as Global Precipitation Measurement (GPM) constellation. In this paper, a method is developed to generate mask over continental Australia using 10, 18, and 37 GHz vertical and horizontal polarized radiance observations from GPM Microwave Imager data. Major factors considered are diurnal variation, second Stokes parameter, spatial homogeneity, and surface emissivity. Major advantage of this tool is its minimal, almost negligible, dependence on any external ancillary modeled or observed data.

Classifying Precipitation Types in Tropical Cyclones Using the NRL 37 GHz Color Product

AbstractThe tropical cyclone (TC) webpage developed by the U.S. Naval Research Lab (NRL) has been widely used by the community for real‐time TC monitoring and forecasting. Using 14 years of Tropical Rainfall Measuring Mission Microwave Imager and Precipitation Radar data in TCs, precipitation/convection types are quantitatively classified based on vertical radar profiles corresponding to seven different color regions in the NRL's 37 GHz color composite product. The bright cyan and all pink (including pure pink, bright cyan/pink, and weak cyan/pink, and green/pink) regions are identified as nearly 100% precipitative regions. The bright cyan and pure pink regions are shallow and deep convective precipitation, respectively, while the green/pink, weak cyan/pink, and bright cyan/pink regions are mainly stratiform rain. Eighty‐four percent of pixels in the weak cyan region are precipitating, including either shallow convective, stratiform, or anvil precipitation. Seventy‐six percent of pixels in the green region are nonprecipitating pixels, and 24% of them are very lightly precipitating pixels, which could be contaminated from clouds with high liquid water content. The ability to perform TC precipitation‐type analysis of this detail (convective versus stratiform and deep versus shallow) was previously only possible using radar‐based classification algorithms. Using the 37 GHz precipitation‐type analysis, a significant increase of coverage of stratiform precipitation and shallow convection in the inner core is found between 3 and 21 hr before the onset of TC rapid intensification.

Indonesia sea surface temperature from TRMM Microwave Imaging (TMI) sensor

We analysis the Tropical Rainfall Measuring Mission's (TRMM) Microwave Imager (TMI) data to monitor the sea surface temperature (SST) of Indonesia waters for a decade of 2005-2014. The TMI SST data shows the seasonal and interannual SST in Indonesian waters. In general, the SST average was highest in March-May period with SST average was 29.4°C, and the lowest was in June – August period with the SST average was 28.5°C. The monthly SST average fluctuation of Indonesian waters for 10 years tends to increase. The lowest SST average of Indonesia occurred in August 2006 with the SST average was 27.6° C, while the maximum occurred in May 2014 with the monthly SST average temperature was 29.9 ° C.

Comparisons of remote sensing and reanalysis soil moisture products over the Tibetan Plateau, China

The availability of reliable long-term and large scale soil moisture observation datasets over the Tibetan Plateau has been of more concern in recent years. It is therefore necessary to verify any such data to have a better guide to their usage. In this study, we have compared satellite soil moisture retrievals of the Special Sensor Microwave Imager (SSM/I), with ERA-Interim/Land and Global Land Data Assimilation System (GLDAS-1) soil moisture products over the Tibetan Plateau. They are found to be largely different from each other regarding the soil moisture magnitudes in spring, summer and autumn, with those of the ERA-Interim/Land particularly higher than the other datasets in the eastern plateau. The SSM/I and the ERA-Interim/Land data are in good agreement considering their seasonal dynamics, which are however considerably different from the GLDAS-1 models (CLM, Noah, Mosaic and VIC). For interannual variations, it is striking to find strong negative correlations (R<−0.6) between the SSM/I and the ERA-Interim/Land data in spring and autumn, especially over the central plateau. The SSM/I data is found to have also strong negative correlations (R<−0.4) with data of the four GLDAS-1 models in the eastern plateau in spring; and such negative correlations appear in central and western plateau in autumn. In summer, the SSM/I data appears to be positively correlated with the ERA-Interim/Land and all the GLDAS-1 data over the plateau. The negative correlations indicate these data have quite different temporal dynamics for their interannual variations, especially in spring and autumn. A look at the spatial variations revealed that the SSM/I data shows a north-south seesaw pattern in all seasons. The GLDAS-1 Mosaic, Noah and CLM data are able to represent this seesaw pattern principally, which however, can hardly be seen in the ERA-Interim/Land and the GLDAS-1 VIC data. Our results have indicated significant uncertainties in the soil moisture data from remote sensing and reanalysis sources; and we have to use such data with great care.