Water Vapor Channel Research Articles

Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation

Abstract. On 24 September 2021, JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the Model Prediction Across Scales – Atmosphere (MPAS-A) built on the software framework of the Joint Effort for Data assimilation Integration (JEDI) was publicly released for community use. Operating directly on the native MPAS unstructured mesh, JEDI-MPAS capabilities include three-dimensional variational (3DVar) and ensemble–variational (EnVar) schemes as well as the ensemble of DA (EDA) technique. On the observation side, one advanced feature in JEDI-MPAS is the full all-sky approach for satellite radiance DA with the introduction of hydrometeor analysis variables. This paper describes the formulation and implementation of EnVar for JEDI-MPAS. JEDI-MPAS 1.0.0 is evaluated with month-long cycling 3DEnVar experiments with a global 30–60 km dual-resolution configuration. The robustness and credible performance of JEDI-MPAS are demonstrated by establishing a benchmark non-radiance DA experiment, then incrementally adding microwave radiances from three sources: Advanced Microwave Sounding Unit-A (AMSU-A) temperature sounding channels in clear-sky scenes, AMSU-A window channels in all-sky scenes, and Microwave Humidity Sounder (MHS) water vapor channels in clear-sky scenes. JEDI-MPAS 3DEnVar behaves well with a substantial and significant positive impact obtained for almost all aspects of forecast verification when progressively adding more microwave radiance data. In particular, the day 5 forecast of the best-performing JEDI-MPAS experiment yields an anomaly correlation coefficient (ACC) of 0.8 for 500 hPa geopotential height, a gap of roughly a half day when compared to cold-start forecasts initialized from operational analyses of the National Centers for Environmental Prediction, whose ACC does not drop to 0.8 until a lead time of 5.5 d. This indicates JEDI-MPAS's great potential for both research and operations.

Performance Evaluation and Noise Mitigation of the FY-3E Microwave Humidity Sounder

Fengyun-3E, which is equipped with MicroWave Humidity Sounder 2 (MWHS-2) for detecting both temperature and humidity, is China’s latest polar orbiting meteorological satellite and China’s first satellite in early-morning orbit. The observation bias and observation error characteristics of MWHS-2 are evaluated by using ERA-5 as the background field. The results show that the biases range from −4 to 2 K, and the observation errors are within 1.5 K except for the window channels. A further analysis of the dependence on the scanning angles indicates that observation errors for the window channels and the 118-GHz channels increase with decreasing scanning angles. Observation errors of the window channels and water vapor channels are also latitudinally dependent, and the maximum errors in the high latitudes reach 2.0 K, while those in the tropical regions are approximately 0.8 K. In addition, the observed brightness temperature of FY-3E MWHS-2 is accompanied by striping noises along the track and high-frequency oscillation noises along the scanlines. The noise mitigation results show that both noises are approximately 0.2 K.

The Space Conceptual Models and Water Vapor Characteristics of Typical Rainstorms during Plum Rain Season

Based on conventional observation data from the China Meteorological Administration (CMA) and reanalysis data from the American National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) between 2012 and 2021, combined with the meteorological analysis, composite synthesis, and water vapor trajectory analysis, the weather circulations of typical rainstorms during the 10 years can be divided into 4 categories: Static Front Pattern (SFP), Subtropical High Edge Pattern (SHEP), Northeast Cold Vortex Pattern (NCVP), and Low-Level Vortex and Shear Pattern (LLVSP). The SHEP and SFP rainstorms have the characteristics of long duration and wide range, while the NCVP rainstorms are characterized by mobility and disaster weather accompaniment. The daily precipitation of LLVSP cases has extremity feature. The occurrence and development of rainstorms are well coordinated with the systems on lower levels. The main water vapor channel in lower layers of the SFP cases is from the South China Sea, while it is from Bohai for the NCVP cases and the Bay of Bengal for the SHEP and LLVSP cases. The main water vapor channel in middle layers is from the Bay of Bengal because of the affection of the southwest air flow. The south boundary of the MLYRB is the most important water vapor input boundary, followed by the west boundary, while the East and North boundaries are the outflow boundaries. During the rainstorms, the low-level water vapor is exuberant with low-level water vapor convergence much stronger than the high-level divergence. Among the four types of rainstorms, the NCVP cases provide the most abundant low-level water vapor convergence, resulting in the strongest short-term precipitation among the four types. Combined with water vapor transportation and convergence, the refined spatial conceptual models of the four types of rainstorms can better judge the process intensity and falling area and provide reference for disastrous weather forecast and early warning.

Impacts of FY-4A AGRI Radiance Data Assimilation on the Forecast of the Super Typhoon “In-Fa” (2021)

This study assessed the impact of assimilating the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) observations on the Super Typhoon “In-Fa” event based on the Weather Research and Forecasting Data Assimilation (WRFDA) system of the three-dimensional variational data assimilation (3DVAR) method. It was found that the two water vapor channels 9–10 from the full-disk AGRI datasets yield relatively stable results in terms of the track forecast of In-Fa. A new cloud-detection method using a Particle Filter (PF) was firstly employed to remove the cloud-affected observations by identifying the channel’s weighting function. Compared to the other cloud-detection schemes based on the AGRI “Cloud_Binary_Mask” (CLM) products, the PF method is conducive to reducing the track error of typhoon prediction after improving the utilization of observations under clear-sky conditions. Furthermore, the proposed cycling assimilation scheme has a potential positive effect on the intensity forecast of In-Fa. It seems that assimilating the FY-4A AGRI radiance data improves the predictability of Typhoon In-Fa by adjusting the atmospheric environment.

Assessing FY-3E HIRAS-II Radiance Accuracy Using AHI and MERSI-LL

The FY-3E/HIRAS-II (Hyperspectral Infrared Atmospheric Sounder-II), as an infrared hyperspectral instrument onboard the world’s first early morning polar-orbiting satellite, plays a major role in improving the accuracy and timeliness of global numerical weather predictions. In order to assess its observation quality, the geometrically, temporally, and spatially matched scene homogeneous HIRAS-II hyperspectral observations were convolved to the channels corresponding to the Himawari-8/AHI (Advanced Himawari Imager) and FY-3E/MERSI-LL (Medium-Resolution Spectral Imager) imagers from 15 March to 21 April 2022, and their brightness temperature deviation characteristics were statistically calculated in this paper. The results show that the HIRAS-II in-orbit observed brightness temperatures are slightly warmer than the AHI observations in all the matched AHI channels (long wave infrared channel 8 to channel 16) with a mean brightness temperature bias less than 0.65 K. The bias of the atmospheric absorption channel is slightly larger than that of the window channel. A standard deviation less than 0.31 K and a correlation coefficient higher than 0.98 in all channels means that the quality of the observation is satisfactory. The thresholds chosen for the colocation approximation factors (e.g., observation geometry angle, scene uniformity, observation azimuth, and observation time) for matching the HIRAS-II with AHI contribute little and negligible uncertainty to the bias assessment, so the difference between the two observed radiations is considered to be mainly from the systematic bias of the two-instrument measurement. Compared with MERSI-LL window channel 5, the observations of both instruments are very close, with a mean bias of 0.002 K and a standard deviation of 0.31 K. The mean brightness temperature bias (HIRAS-II minus MERSI-LL) of the MERSI-LL water vapor channel 4 is 0.66 K with a standard deviation of 0.22 K. The mean brightness temperature bias of channel 6 and channel 7 is 0.63 K (the standard deviation is 0.36 K) and 0.5 K (the standard deviation is 0.3 K), respectively. The biases of channel 4 are significantly and positively correlated with the target scene temperature, and the biases of channel 6 and 7 show a U-shaped change with the increase in the scene temperature, and the biases are smallest (close to 0 K) when the scene temperature is between 250 K and 280 K. The statistical characteristics of the HIRAS-II–MERSI-LL difference vary minimally and almost constantly over a period of time, indicating that the performance of the HIRAS-II instrument is stable.

Effects of Low-Frequency Oscillation at Different Latitudes on Summer Precipitation in Flood and Drought Years in Southern China

Based on the daily precipitation data from 753 meteorological stations provided by the National Meteorological Information Center (China) and the daily reanalysis data from NCEP/NCAR and ERA5 during the period from 1980 to 2020, the low-frequency (LF) precipitation characteristics of the typical summer flood and drought years in southern China and their relation to the LF atmospheric circulation at different latitudes are compared and analyzed, and extended-range forecasting signals are given. The results show that: (a) In both flood and drought years, summer precipitation in southern China is controlled by 10–20 day oscillation (quasi-biweekly oscillation, QBWO); (b) LF convection is active in southern China in both flood and drought years, but the convective center is southward in flood years, and the vertical meridional circulation is stronger. The key circulation systems of 500 hPa LF height field in flood and drought years include LF “two ridges and one trough” and LF “+”, “−”, “+” East Asia Pacific (EAP) teleconnection wave train in mid-high latitudes of Eurasia. However, the “two ridges and one trough” in flood years are more westward and meridional than in drought years, and the LF Subtropical High is stronger and more extensive, with more significant westward extension; (c) In flood (drought) years, there is northerly and then westerly (central westerly) dry-cold, northeasterly wet-cold, southwesterly (none), and southeasterly (including southerly across the equator) wet-warm water vapor channels. The sources of dry and wet cold air in flood (drought) years are located near Novaya Zemlya (the eastern West Siberian Plain), the Yellow Sea, and the Bohai Sea (Sea of Japan). Additionally, the sources of wet-warm water vapor include the Arabian Sea, the Bay of Bengal, the western Pacific Ocean, and the sea area of northeastern Australia (the western Pacific Ocean and the northern sea area of Australia); and (d) The LF predictive signals of outgoing longwave radiation (OLR) appear on −11 days, while the signals of the 500 hPa height field are on −9 days. There are both westward and eastward propagation predictive signals in flood years, and only westward spread signals in drought years.

Identification of synoptic patterns for extreme rainfall events associated with landfalling typhoons in China during 1960–2020

Extreme rainfall associated with landfalling typhoon (ERLTC) can cause severe disasters and economic impacts throughout China. Improving the accuracy of ERLTC forecasts is therefore crucial in disaster prevention and mitigation. The top 26 ERLTC events in China during 1960–2020 are investigated based on multi-source datasets. These ERLTC events are categorized into five main types according to the geographical location of the extreme precipitation and its position relative to the tropical cyclone (TC) center, namely: the typhoon inner-core rainfall in Taiwan (TWIC), typhoon inverted trough rainfall in Taiwan (TWIT), weak typhoon rainfall in Hainan (HNWK), strong typhoon rainfall in Zhejiang (ZJST) and inland typhoon remnant rainfall (ILRM). All the ERLTC events occurred in the weakening stage of TC after reaching its lifetime maximum intensity in convective cloud (TBB ≤ −32 °C) regions over complex local terrain. The translational speeds of 20 TCs (76.9% of the total) were smaller than the climatological average (20.6 km h−1) during the extreme precipitation events. The differences are as follows: the TWIC and TWIT types are featured with different season, track and water vapor channel although both occurred in Taiwan. The other three types are distinguished by spinning track and strong convective cloud for HNWK type, strong TC intensity and binary TC interactions for ZJST type; and stagnation and strong westerly trough activity for ILRM type, respectively. These results are expected to provide useful clues for an in-depth understanding of ERLTC events over China.

Effects of Direct Assimilation of FY-4A AGRI Water Vapor Channels on the Meiyu Heavy-Rainfall Quantitative Precipitation Forecasts

In this study, the regional Weather Research and Forecasting model (WRF)-based quantitative precipitation forecasts (QPFs) are conducted for an extreme Meiyu rainfall event over East Asia in 2020. The data of water vapor channels 9 and 10 from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Fengyun-4A (FY-4A) satellite are assimilated through the Gridpoint Statistical Interpolation (GSI) system. It shows that a reasonable amount of assimilated AGRI data can produce reasonable water vapor increments, compared to the too sparse or dense assimilated AGRI observations. In addition, the critical success indexes (CSIs) of the precipitation forecasts within 72 h are obviously improved. The enhanced variational bias correction (VarBC) scheme is applied to remove the air-mass and scan-angle biases, and the mean observation-minus-background (O − B) values before and after the VarBC of channel 9 are −1.185 and 0.02 K, respectively, and those of channel 10 are −0.559 and −0.01 K, respectively. Assimilating the upper-level channel 9 data of AGRI (EXP_WV9) lead to a neutral-to-positive effect on QPFs, compared to the control run (CTL), which is based on the assimilation of Advanced Microwave Sounding Unit-A (AMSU-A) data. In particular, the CSIs from 42 to 72 h are significantly improved. However, the assimilation of the AGRI channel 10 (EXP_WV10) shows a neutral-to-negative effect on QPFs in this study, probably due to the complicated surface situations. This study confirms the feasibility of assimilating the water vapor channel data of FY4A AGRI in the GSI system and highlights the importance of assimilating AGRI channel 9 data to improve the QPFs of the Meiyu rainfall event.

A Novel Approach for the Global Detection and Nowcasting of Deep Convection and Thunderstorms

Thunderstorms are among the most common and most dangerous meteorological hazards in the world. They cause lightning and can lead to strong wind gusts, squall lines, hail and heavy precipitation combined with flooding, and therefore pose a threat to health and life, can cause enormous property damage and also endanger flight safety. Monitoring and forecast of thunderstorms are, therefore, important topics. In this work, a novel method for the detection and forecast of thunderstorms and strong convection is presented. The detection is based on the global GLD360 lightning data in combination with satellite information from the satellite series Meteosat, HIMAWARI and GOES, covering the complete geostationary ring. Three severity levels are defined depending on the occurrence of lightning and the brightness temperature difference of the water vapour channels and the infrared window channel (∼10.8 μm). The detection of thunderstorms and strong convection is the basis for the nowcasting up to 2 h, which is performed with the optical flow method TV-L1. This method provides the needed atmospheric motion vectors for the extrapolation of the thunderstorm movement. Both, the validation results as well as the feedback of the customers show the great value of the new NowCastSat-Aviation (NCS-A) method. For example, the Critical Success Index (CSI) is, with 0.64, still quite high for the 60 min forecast of severe thunderstorms. The method is operated 24/7 by the German Weather Service (DWD), and is used to provide thunderstorm information to aviation customers and the central weather forecast unit of DWD.

Seasonal Variation in Microphysical Characteristics of Precipitation at the Entrance of Water Vapor Channel in Yarlung Zangbo Grand Canyon

Mêdog is located at the entrance of the water vapor channel in the Yarlung Zangbo Grand Canyon (YGC). This area has the largest annual accumulated rainfall totals and precipitation frequency on the Tibetan Plateau (TP). This paper investigates the seasonal variation in raindrop size distribution (DSD) characteristics in Mêdog based on disdrometer observations from 1 July 2019 to 30 June 2020. The DSD characteristics are examined under six rain rate classes and two rainfall types (stratiform and convective) in the winter, premonsoon, monsoon and postmonsoon periods. The highest (lowest) concentration of small raindrops is observed in monsoon (winter) precipitation, whereas large raindrops predominate in premonsoon precipitation. For stratiform rainfall, the mean mass-weighted mean diameter (Dm) exhibits overlooked differences in the four periods, while the mean normalized intercept parameter (Nw) is significantly higher in the monsoon period than in the other three periods. The convective rainfall in the monsoon and postmonsoon periods is characterized by a high concentration of limited-size drops and can be classified as maritime-like. This is probably attributed to abundant warm and humid airflow transported by the Indian Ocean monsoon into Mêdog. The westerly winds prevail over the TP during the premonsoon period, and thereby the premonsoon convective rainfall in Mêdog has a larger mean Dm and a lower mean Nw. In addition, the relationships of radar reflectivity Z and rain rate R for different precipitation types in different periods are also derived. A better understanding of the seasonal variation in the microphysical characteristics of precipitation in Mêdog is important for improving the microphysical parameterization scheme and the precipitation forecast of models on the TP.

Assessment of the Consistency and Stability of CrIS Infrared Observations Using COSMIC-2 Radio Occultation Data over Ocean

The accuracy of brightness temperature (BT) from the Cross-track Infrared Sounder (CrIS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite and NOAA-20 is estimated using the Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2) radio occultation (RO) wet retrievals (temperature and water vapor profiles) as input to the Community Radiative Transfer Model (CRTM). The matchup criteria between RO and CrIS observations are time less than 30 min, a distance less than 50 km, and over oceans to reduce the collocation and simulation uncertainty. Based on the information provided in the CrIS and RO observations, only upper temperature sounding channels with weighting function peak height (WFPH) above 200 hPa (~12 km) from the CrIS longwave infrared (LWIR) and shortwave infrared (SWIR) bands and water vapor channels from the CrIS mid-wave infrared (MWIR) band with WFPH above 500 hPa (~6.3 km) are selected for comparison to minimize the impacts from the surface emission, cloud absorption/scattering, and atmospheric gaseous absorption. The absolute differences between CrIS observations and their CRTM simulations using RO data as input are less than 1.0 K for the majority of those selected channels. The double differences between CrIS observations on NOAA-20 and S-NPP using CRTM simulations as transfer references are very stable. They range from −0.05 K to 0.15 K for LWIR channels and −0.20 K to 0.10 K for SWIR channels during the two years from 1 October 2019 to 30 September 2021. For MWIR channels, the double differences range from −0.15 K to 0.25 K but have significant variations in both daily mean and monthly mean time series. The results provide ways to understand the qualities of RO retrieval and CrIS measurements: RO data can be used to assess the consistency and stability of CrIS observations quantitatively, and CrIS measurements have the quality to assess the quality and stability of RO retrievals.

First observation of mercury species on an important water vapor channel in the southeastern Tibetan Plateau

Abstract. The Tibetan Plateau is generally considered to be a significantly clean area owing to its high altitude; however, the transport of atmospheric pollutants from the Indian subcontinent to the Tibetan Plateau has influenced the Tibetan environments. Nyingchi is located at the end of an important water vapor channel. In this study, continuous monitoring of gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particle-bound mercury (PBM) was conducted in Nyingchi from 30 March to 3 September 2019, to study the influence of the Indian summer monsoon (ISM) on the origin, transport, and behavior of Hg. The GEM and PBM during the preceding Indian summer monsoon (PISM) period (1.20±0.35 ng m−3 and 11.4±4.8 pg m−3 for GEM and PBM, respectively) were significantly higher than those during the ISM period (0.95±0.21 ng m−3, and 8.8±6.0 pg m−3); the GOM during the PISM period (13.5±7.3 pg m−3) was almost at the same level as that during the ISM period (12.7±14.3 pg m−3). The average GEM concentration in the Nyingchi region, obtained using passive sampler, was 1.12±0.28 ng m−3 (from 4 April 2019 to 31 March 2020). The GEM concentration showed that the sampling area was very clean compared to other high-altitude sites. The GEM has several patterns of diurnal variation during different periods. Stable high GEM concentrations occur at night and low concentrations occur in the afternoon during PISM, which may be related to the nocturnal boundary layer structure. High values occurring in the late afternoon during the ISM may be related to long-range transport. Low concentrations of GEM observed during the morning in the ISM may originate from vegetation effects. The results of the trajectory model demonstrate that the sources of pollutants at Nyingchi are different with different circulation patterns. During westerly circulation in the PISM period, pollutants mainly originate from central India, northeastern India, and central Tibet. During the ISM period, the pollutants mainly originate from the southern part of the SET site. The strong precipitation and vegetation effects on Hg species during the ISM resulted in low Hg concentrations transmitted to Nyingchi during this period. Further, principal component analysis showed that long-distance transport, local emissions, meteorological factors, and snowmelt factors are the main factors affecting the local Hg concentration in Nyingchi. Long-distance transport factor dominates during PISM and ISM3, while local emissions is the major contributor between PISM and ISM3. Our results reveal the Hg species distribution and possible sources of the most important water vapor channel in the Tibetan Plateau and could serve as a basis for further transboundary transport flux calculations.

Radiative Transfer Model Simulations for Ground-Based Microwave Radiometers in North China

Ground-based microwave radiometer profilers (MWRPs) are widely used to provide high-temporal resolution atmospheric temperature and humidity profiles. The quality of the observed brightness temperature (TB) from MWRPs is key for retrieving accurate atmospheric profiles. In this study, TB simulations derived from a radiative transfer model (RTM) were used to assess the quality of TB observations. Two types of atmospheric profile data (conventional radiosonde and ERA5 reanalysis) were combined with the RTM to obtain TB simulations, then compared with corresponding observations from three MWRPs located in different places in North China to investigate the influence of input atmospheric profiles on TB simulations and evaluate the quality of TB observations from the three MWRPs. The comparisons of the matching samples under clear-sky conditions showed that TB simulations derived from both radiosonde and ERA5 profiles were very close to the TB observations from most of the MWRP channels; however, the correlation was lower and the bias was obvious at 51.26 GHz and 52.28 GHz, which indicates that the oxygen absorption component in the RTM needs to be improved for lower-frequency temperature channels. The difference in location of the radiosonde and MWRP sites affected the TB simulations for the water vapor channels, but had little impact on temperature channels that are insensitive to humidity. Comparisons of both simulations (ERA5 and Radiosonde) and the corresponding TB observations from the three sites indicated that the water vapor channels observation quality for the MWRP located in southern Beijing needs improvement. For the two types of profile data, ERA5 profiles have a more positive effect on TB simulations in the water vapor channels, such as enhanced consistence, reduced bias and standard deviation between simulations and observations for those MWRPs located away from the radiosonde station. Therefore, hourly ERA5 data are an optimal option in terms of compensating for limited radiosonde measurements and enhancing the monitoring quality of MWRP observations within 24 h.

Evaluation of ALADIN NWP model forecasts by IR10.8 μm and WV06.2 μm brightness temperatures measured by the geostationary satellite Meteosat Second Generation

An evaluation of forecasts by ALADIN numerical weather prediction model is performed by comparing synthetic and measured IR10.8 μm (Infrared channel with a wavelength of 10.8 μm) and WV06.2 μm (Water vapour channel with a wavelength of 6.2 μm) brightness temperatures (BTs) by the geostationary satellite Meteosat Second Generation. To calculate synthetic satellite images, we used a radiative transfer model RTTOV v11.3, as implemented in the ALADIN model. The evaluation of the forecasts is performed for summer month June 2020 and winter month February 2021 and for lead times from 1 to 72 h. The model horizontal resolution is 2.3 km and the verification domain covers a large part of Europe. As for verification parameters, we calculated bias, root-mean-square error, correlation coefficient (PC), Fraction Skill Score and a technique that take into account the relative position of the verified objects. We diagnosed relationships, measured by PC, between the forecasted and measured BTs on one side and the forecasted and measured precipitation on the other side. We present and discuss verification results in the view of usefulness for model developers.

Impact of Assimilating Advanced Himawari Imager Channel 16 Data on Precipitation Prediction over the Haihe River Basin

Assimilation of high-resolution geostationary satellite data is of great value for precise precipitation prediction in regional basins. The operational geostationary satellite imager carried by the Himawari-8 satellite, Advanced Himawari Imager (AHI), has two additional water vapor channels and four other channels compared with its predecessor, MTSAT-2. However, due to the uncertainty in surface parameters, AHI surface-sensitive channels are usually not assimilated over land, except for the three water vapor channels. Previous research showed that the brightness temperature of AHI channel 16 is much more sensitive to the lower-tropospheric temperature than to surface emissivity, which is similar to the three water vapor channels 8–10. As a follow-up work, this paper evaluates the effectiveness of assimilating brightness temperature observations over land from both the three AHI water vapor channels and channel 16 to improve watershed precipitation forecasting through both case analysis (in the Haihe River basin, China) and batch tests. It is found that assimilating AHI channel 16 can improve the upstream near-surface atmospheric temperature forecast, which in turn affects the development of downstream weather systems. The precipitation forecasting test results indicate that adding the terrestrial observations of channel 16 to the assimilation of AHI data can improve short-term precipitation forecasting in the basin.

Quality Scoring of the Fengyun 4A Clear Sky Radiance Product

The Clear Sky Radiance (CSR) product has been widely used instead of Level 1 (L1) geostationary imager data in data assimilation for numerical weather prediction due to its many advantages concerning superobservation methodology. In this study, CSR was produced in two water vapor channels (channels 9 and channel 10, with wavelengths at 5.8–6.7 μm and 6.9–7.3 μm) of the Advanced Geostationary Radiation Imager aboard Fengyun 4A. The root mean square error (RMSE) between CSR observations and backgrounds was used as a quality flag and was predicted by cloud cover, standard deviation (STD), surface type, and elevation of a CSR field of view (FOV). Then, a centesimal scoring system based on the predicted RMSE was set to a CSR FOV that indicates its percentile point in the quality distribution of the whole FOV. Validations of the scoring system demonstrated that the biases of the predicted RMSE were small for all FOVs and that the score was consistent with the predicted RMSE, especially for FOVs with high scores. We suggest using this score for quality control (QC) to replace the QC of cloud cover, STD, and elevation of CSR, and we propose 40 points as the QC threshold for the two channels, above which the predicted RMSE of a CSR is superior to the RMSE of averaged clear-sky L1 data.

Enhanced joint effects of ENSO and IOD on Southeast China winter precipitation after 1980s

Based on the NCEP/NCAR reanalysis data, the COBE sea surface temperature (SST) and the GPCC precipitation, the influences of El Niño-South Oscillation (ENSO) on the variability of winter rainfall anomalies in Southeast China is analyzed under the synergistic effect of Indian Ocean Dipole (IOD). Winter precipitation and atmospheric circulation of the years of IOD concurring with ENSO are compared with single IOD or ENSO, to reveal the mechanism of synergistic effects on the variability of winter rainfall anomalies in Southeast China. The results show that the correlation between IOD/ENSO and the winter precipitation in Southeast China has increased since 1980s. These correlations were significant in years of IOD and ENSO co-occurrence compared to years of IOD or ENSO only, which is mainly due to the lagged atmospheric thermal and dynamic responses to an IOD forcing in synergy with ENSO. The positive IOD (PIOD) events can trigger and modify the anticyclone to the east of India, which transports moisture from the tropical Indian Ocean to Southeast China. In addition, El Niño events can strengthen the abnormal anticyclone over Philippines in winter, which is conducive to maintain the water vapor channel from the tropical western Pacific to Southeast China. Information flow analysis shows that the causalities between IOD and ENSO were enhanced after 1980s, causing the significant increase in the frequency of winter abnormal precipitation in the years of IOD and ENSO concurrence. Furthermore, the higher frequency of PIOD with El Niño (compared to negative IOD with La Niña) attained a ratio of 2:1 after the 1980s, enhancing the Southeast China winter precipitation events associated with IOD and ENSO and the generation of interdecadal variability. This study is helpful to understand the mechanisms of winter precipitation changes in Southeast China, and to improve the forecast accuracy of winter extreme precipitation events.

An Infrared Atmospheric Sounding Interferometer – New Generation (IASI‐NG) channel selection for numerical weather prediction

AbstractIn the framework of the EUMETSAT Polar System–Second Generation (EPS‐SG) preparation, a new generation of the Infrared Atmospheric Sounding Interferometer (IASI) instrument has been designed. The IASI‐New Generation (IASI‐NG) will measure radiances at a doubled spectral resolution compared to its predecessor and with a signal‐to‐noise ratio improved by a factor of 2. The large amount of data arising from IASI‐NG will present many challenges for data transmission, storage and assimilation. Moreover, the full set of measured radiances will not be exploitable in an operational numerical weather prediction (NWP) context. For these reasons, an appropriate IASI‐NG channel selection in needed, aiming to select the most informative channels for NWP models. Therefore, the standard iterative channel selection methodology, based on the optimal linear estimation theory and assuming spectrally correlated errors, has been applied to a set of simulated data of the IASI‐NG spectrum. The entire simulated IASI‐NG spectrum has been first investigated, while finally focusing the channel selection procedure on the most interesting wavelength ranges for the assimilation. Through this process, a total of 500 channels have been chosen to serve as a basis for future channel selections to be provided to NWP centres – 277 temperature, 23 surface‐sensitive and 200 water vapour channels. One‐dimensional variational (1D‐Var) assimilation experiments show that using this selected set of channels leads to a reduction of the standard deviation of the error in temperature (up to 30%) and water vapour (up to 50%) profiles with respect to the information.

Classifying precipitation from GEO satellite observations: Diagnostic model

AbstractImprovements in remote‐sensing capability and improvements in artificial intelligence have created significant opportunities to advance understanding of precipitation processes. While highly advanced Machine Learning (ML) techniques improve the accuracy of precipitation retrievals, how these observations contribute to our understanding of precipitation processes remains an underexplored research question. In a companion manuscript, a precipitation‐type prognostic ML model is developed by deriving predictors from the Advanced Baseline Imager (ABI) sensor on board Geostationary Observing Environmental Satellite (GOES)‐16. In this study, these predictors are linked to different precipitation processes. It is observed that satellite observations are important in separating Rain and No‐Rain areas. For stratiform precipitation types, predictors related to atmospheric moisture content, such as relative humidity and precipitable water, are the most important predictors, while for convective types, predictors such as 850–500 hPa lapse rate and convective available potential energy (CAPE) are more important. The diagnostic analysis confirms the benefit of spatial textures derived from ABI observations to improve the classification accuracy. It is recommended to combine the heritage water vapour channel T6.2 with the infrared T11.2 channel for improved precipitation classification. Overall, this study provides guidance to atmospheric and remote‐sensing scientists on a large array of predictors that can be used from geostationary satellites and multispectral sensors for precipitation studies.

Correlation Structures between Satellite All-Sky Infrared Brightness Temperatures and the Atmospheric State at Storm Scales

This study explores the structures of the correlations between infrared (IR) brightness temperatures (BTs) from the three water vapor channels of the Advanced Baseline Imager (ABI) onboard the GOES-16 satellite and the atmospheric state. Ensemble-based data assimilation techniques such as the ensemble Kalman filter (EnKF) rely on correlations to propagate innovations of BTs to increments of model state variables. Because the three water vapor channels are sensitive to moisture in different layers of the troposphere, the heights of the strongest correlations between these channels and moisture in clear-sky regions are closely related to the peaks of their respective weighting functions. In cloudy regions, the strongest correlations appear at the cloud tops of deep clouds, and ice hydrometeors generally have stronger correlations with BT than liquid hydrometeors. The magnitudes of the correlations decrease from the peak value in a column with both vertical and horizontal distance. Just how the correlations decrease depend on both the cloud scenes and the cloud structures, as well as the model variables. Horizontal correlations between BTs and moisture, as well as hydrometeors, in fully cloudy regions decrease to almost 0 at about 30 km. The horizontal correlations with atmospheric state variables in clear-sky regions are broader, maintaining non-zero values out to ∼100 km. The results in this study provide information on the proper choice of cut-off radii in horizontal and vertical localization schemes for the assimilation of BTs. They also provide insights on the most efficient and effective use of the different water vapor channels.