Accurate precipitation detection is one of the most important factors in satellite data assimilation, due to the large uncertainties associated with precipitation properties in radiative transfer models and numerical weather prediction (NWP) models. In this paper, a method to achieve remote sensing of precipitation and classify its intensity over land using a co-located ground-based radar network is described. This method is intended to characterize the O−B biases for the microwave humidity sounder -2 (MWHS-2) under four categories of precipitation: precipitation-free (0–5 dBZ), light precipitation (5–20 dBZ), moderate precipitation (20–35 dBZ), and intense precipitation (>35 dBZ). Additionally, O represents the observed brightness temperature (TB) of the satellite and B is the simulated TB from the model background field using the radiative transfer model. Thresholds for the brightness temperature differences between channels, as well as the order relation between the differences, exhibited a good estimation of precipitation. It is demonstrated that differences between observations and simulations were predominantly due to the cases in which radar reflectivity was above 15 dBZ. For most channels, the biases and standard deviations of O−B increased with precipitation intensity. Specifically, it is noted that for channel 11 (183.31 ± 1 GHz), the standard deviations of O−B under moderate and intense precipitation were even smaller than those under light precipitation and precipitation-free conditions. Likewise, abnormal results can also be seen for channel 4 (118.75 ± 0.3 GHz).
... To make better use of microwave radiance observations for data assimilation, the removal of data contaminated by hydrometeor particles is important. The most common cloud detection method allows for the detection of precipitation based on the deviation between observation and simulation brightness temperature (O-B) of satellite channels [22]. A scattering index [linear regression model of channel 15 and channels 1-3 of AMSU-A, used by the Advanced TIROS Operational Vertical Sounder (ATOVS) and Advanced Very High Resolution Radiometer (AVHRR) Pre-processing Package (AAPP)] has also been employed by English et al. [23]. ...
... Then the value of composite reflectivity whose measure time is closest to the time when MWHS-2 passes the East China and measure range is in the area of MWHS-2's filed-of-view (FOV), is selected to calculate the average value in each FOV. Radar reflectivity factor is often used for cloud detection [21]- [25]. In this study, the scenes are flagged as cloudy when the radar reflectivity exceeds 5 dBZ. ...
This paper presents a stand-alone cloud detection algorithm over land (CDL) for Microwave Humidity Sounder -2 (MWHS-2), which is characterized by the first operational satellite sensor measuring 118.75 GHz. The CDL is based on the advanced machine learning (ML) algorithm Gradient Boosting Decision Tree (GBDT), which achieves the state-of-the-art performance on tabular data, with high accuracy, fast training speed, great generalization ability, and weight factor ranking of predictors (or features). Given that the new generation weather radar of China (CINRAD) provides improved cloud information with extensive temporal-spatial coverage, the observations from CINRAD are used to train the algorithm in this study. There are four groups of radiometric information employed to evaluate the CDL: all frequency ranges from MWHS-2 (all-algorithm), the humidity channels near 183.31 GHz (hum-algorithm), the temperature channels near 118.75 GHz (tem-algorithm), and the window channels at 89 and 150 GHz (win-algorithm). It is revealed that the tem-algorithm (around 118.75 GHz) has a superior performance for CDL along with the optimal values of most evaluation metrics. Although the all-algorithm uses all available frequencies, it shows inferior ability for CDL. Followed are the win-algorithm and hum-algorithm, and the win-algorithm performs better. The analysis also indicates that the latitude, zenith angle, and the azimuth are the top ranking features for all four algorithms. The presented algorithm CDL can be applied in the quality control processes of assimilating microwave (MW) radiances or in the retrieval of atmospheric and surface parameters for cloud filtering.
... To make better use of microwave radiance observations for data assimilation, the removal of data contaminated by hydrometeor particles is important. The most common cloud detection method allows for the detection of precipitation based on the deviation between observation and simulation brightness temperature (O-B) of satellite channels [22]. A scattering index [linear regression model of channel 15 and channels 1-3 of AMSU-A, used by the Advanced TIROS Operational Vertical Sounder (ATOVS) and Advanced Very High Resolution Radiometer (AVHRR) Pre-processing Package (AAPP)] has also been employed by English et al. [23]. ...
To make better use of microwave radiance observations for data assimilation, removal of radiances contaminated by hydrometeor particles is one of the most important steps. Generally, all observations below the middle troposphere are eliminated before the analysis when precipitation is present. However, the altitude of the cloud top varies; when the weighting function peak height of a channel is higher than the altitude of the cloud top, observations are not affected by the absorption or scattering of cloud particles. Thus, the radiative transfer calculation can be performed under a clear sky scenario. In this paper, a dynamic channel selection (DCS) method was developed to determine the radiance observations unaffected by clouds under cloudy conditions in assimilation. First, the sensitivity of cloud liquid water (CLW) profiles to radiance from the microwave temperature sounding frequencies was analyzed. It was found that the impact of CLW on transmittance can be neglected where the cloud top height is below the weighting function peak height. Second, three lookup tables were devised through analysis of the impact of cloud fraction and cloud top height on radiance, which is the basis of the DCS method. The unified cloud top height of the Microwave Temperature Sounder (MWTS)-2 fields of view (FOVs) can be calculated by remapping the cloud mask and cloud top height data from the Medium Resolution Spectral Imager-2 (MERSI-2). Observations from various channels may be removed or retained based on real-time dynamic unified cloud top height data. Twelve-hour and long-term time-series brightness temperature simulation experiments both showed that an increase in the amount of observations used for data assimilation of more than 300% can be achieved by application of DCS, but this had no effect on the amount of error. Through DCS, areas of strong precipitation can be accurately identified and removed, and more observations above cloud top height can be included in the data assimilation. The application of DCS to data assimilation will greatly improve the data utilization rate, and therefore allow for more accurate characterization of upper atmospheric circulation.
Reflectivity factor bias caused by radar calibration errors would influence the accuracy of Quantitative Precipitation Estimations (QPE), and further result in spatial discontinuity in Multiple Ground Radars QPE (MGR-QPE) products. Due to sampling differences and random errors, the associated discontinuity cannot be thoroughly solved by the single-radar calibration method. Thus, a multiple-radar synchronous calibration approach was proposed to mitigate the spatial discontinuity of MGR-QPE. Firstly, spatial discontinuity was solved by the intercalibration of adjacent ground radars, and then calibration errors were reduced by referring to the Ku-Band Precipitation Radar (KuPR) carried by the Global Precipitation Measurement (GPM) Core Observatory as a standard reference. Finally, Mosaic Reflectivity and MGR-QPE products with spatial continuity were obtained. Using three S-band operational radars covering the lower reaches of the Yangtze River in China, this method was evaluated under four representative precipitation events. The result showed that: (1) the spatial continuity of reflectivity factor and precipitation estimation fields was significantly improved after bias correction, and the reflectivity differences between adjacent radars were reduced by 78% and 82%, respectively; (2) the MGR-QPE data were closer to gauge observations with the normalized absolute error reducing by 0.05 to 0.12.
The NEMS GFS Aerosol Component Version 2.0 (NGACv2) for global multispecies
aerosol forecast has been developed at the National Centers of Environment
Prediction (NCEP) in collaboration with the NESDIS Center for Satellite
Applications and Research (STAR), the NASA Goddard Space Flight Center
(GSFC), and the University at Albany, State University of New York (SUNYA).
This paper describes the continuous development of the NGAC system at NCEP
after the initial global dust-only forecast implementation (NGAC version 1.0,
NGACv1). With NGACv2, additional sea salt, sulfate, organic carbon, and black
carbon aerosol species were included. The smoke emissions are from the NESDIS
STAR's Global Biomass Burning Product (GBBEPx), blended from the global
biomass burning emission product from a constellation of geostationary
satellites (GBBEP-Geo) and GSFC's Quick Fire Emission Data Version 2 from a
polar-orbiting sensor (QFED2). This implementation advanced the global
aerosol forecast capability and made a step forward toward developing a
global aerosol data assimilation system. The aerosol products from this
system have been used by many applications such as for regional air quality
model lateral boundary conditions, satellite sea surface temperature (SST) physical retrievals, and
the global solar insolation estimation. Positive impacts have been seen in
these applications.
Calibration error, due to ground-based radar (GR) hardware parameters, introduces biases to GR reflectivity factor data. As a result of this, GR observations from the China new-generation weather radar network have sometimes been found to be inconsistent, even when observing precipitation in the same area. In this paper a method, called the Available Best Comparable Data set (ABCD) method, is proposed. The method evaluates the radar reflectivity bias in GR datasets by using TRMM Standard Product (TSP) 2A25 data from Tropical Rainfall Measuring Mission (TRMM) satellite-borne precipitation radar (PR). The analysis is supported by simultaneous measurement data from two precipitation events that occurred in the middle and lower reaches of the Yangtze River between June and July 2010; data were collected during these events by three radars in Nanjing, Changzhou and Nantong (Jiangsu province, China). The ABCD method could correct deviations in the GR reflectivity factors; the GR-corrected values were consistent with those observed by automatic weather stations. The method can be applied to improve the quality of precipitation data products from the GR network and their quantitative application for the southern region in China.
The paper presents a cloud filtering method for upper tropospheric humidity (UTH) measurements at 183.31±1.00 GHz. The method uses two criteria: The difference between the brightness temperatures at 183.31±7.00 and 183.31±1.00 GHz, and a threshold for the brightness temperature at 183.31±1.00 GHz. The robustness of this cloud filter is demonstrated by a mid-latitudes winter case-study. The paper then studies different biases on UTH climatologies. Clouds are associated with high humidity, therefore the dry bias introduced by cloud filtering is discussed and compared to the wet biases introduced by the clouds radiative effect if no filtering is done. This is done by means of a case study, and by means of a stochastic cloud database with representative statistics for midlatitude conditions. The consistent result is that both cloud wet bias (0.8% RH) and cloud filtering dry bias (–2.4% RH) are modest for microwave data, where the numbers given are for the stochastic cloud dataset. This indicates that for microwave data cloud-filtered UTH and unfiltered UTH can be taken as error bounds for errors due to clouds. This is not possible for the more traditional infrared data, since the radiative effect of clouds is much stronger there. The focus of the paper is on midlatitude data, since atmospheric data to test the filter for that case were readily available. The filter is expected to be applicable also to subtropical and tropical data, but should be further validated with case studies similar to the one presented here for those cases.
The capability of all-sky microwave radiance assimilation in the Gridpoint Statistical Interpolation (GSI) analysis system has been developed at the National Centers for Environmental Prediction (NCEP).
This development effort required the adaptation of quality control, observation error assignment, bias correction, and background error covariance to all-sky conditions within the Ensemble-Variational (EnVar) framework. The assimilation of cloudy radiances from the Advanced Microwave Sounding Unit-A
(AMSU-A) microwave radiometer for ocean Field of Views (FOV) is the primary emphasis of this study.
In the original operational hybrid 3D EnVar Global Forecast System (GFS), the clear-sky approach for radiance data assimilation is applied. Changes to data thinning and quality control have allowed all-sky satellite radiances to be assimilated in the GSI. Along with the symmetric observation error assignment, additional situation-dependent observation error inflation is employed for all-sky conditions. Moreover, in addition to the current radiance bias correction, a new bias correction strategy has been applied to all-sky radiances. In this work, the static background error variance and the ensemble spread of cloud water are examined, and the cloud variability from the ensemble forecast in single- and dual- resolution configurations are discussed. Overall, the all-sky approach provides more realistic simulated brightness temperatures and cloud water analysis increments, and improves analysis off the west of the continents by reducing a known bias in stratus. An approximate 10% increase in the use of AMSU-A channels 1 to 5 and a 12% increase for channel 15 are also observed. The all-sky AMSU-A radiance assimilation became operational in the 4D EnVar GFS system upgrade of May 12, 2016.
Gridpoint Statistical Interpolation (GSI) is an assimilation tool that is
used at the National Centers for Environmental Prediction (NCEP) in
operational weather forecasting in the USA. In this article, we describe
implementation of an extension to the GSI for assimilating surface
measurements of PM2.5, PM10, and MODIS aerosol optical depth at
550 nm with WRF-Chem (Weather Research and Forecasting model coupled with Chemistry). We also present illustrative results. In the past, the
aerosol assimilation system has been employed to issue daily PM2.5
forecasts at NOAA/ESRL (Earth System Research Laboratory) and, we believe, it is well tested and mature enough
to be made available for wider use. We provide a package that, in addition to
augmented GSI, consists of software for calculating background error
covariance statistics and for converting in situ and satellite data to BUFR (Binary
Universal Form for the Representation of meteorological data)
format, and sample input files for an assimilation exercise. Thanks to
flexibility in the GSI and coupled meteorology–chemistry of WRF-Chem,
assimilating aerosol observations can be carried out simultaneously with
meteorological data assimilation. Both GSI and WRF-Chem are well documented
with user guides available online. This article is primarily intended to be a
technical note on the implementation of the aerosol assimilation. Its purpose
is also to provide guidance for prospective users of the computer code.
Scientific aspects of aerosol assimilation are also briefly discussed.
Accurate cloud detection is one of the most important factors in satellite data assimilation due to the uncertainties associated with cloud properties and their impacts on satellite-simulated radiances. To enhance the accuracy of cloud detection and improve radiance assimilation for tropical cyclone (TC) forecasts, measurements from the Advanced Microwave Sounding Unit-A (AMSU-A) on board the Aqua satellite and the Advanced Technology Microwave Sounder (ATMS) are collocated with high spatial resolution cloud products from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua and the Visible Infrared Imager Radiometer Suite (VIIRS) on board the Suomi-National Polar-Orbiting Partnership (Suomi-NPP) satellite. The cloud-screened microwave radiance measurements are assimilated for Hurricane Sandy (2012) and Typhoon Haiyan (2013) forecasts using the Weather Research and Forecasting (WRF) Model and the three-dimensional variational (3DVAR)-based Gridpoint Statistical Interpolation (GSI) data assimilation system. Experiments are carried out to determine the optimal thresholds of cloud fraction (CF) for minimizing track and intensity forecast errors. The results indicate that the use of high spatial resolution cloud products can improve the accuracy of TC forecasts by better eliminating cloud-contaminated microwave sounder field-of-views (FOVs). In conclusion, the combination of advanced microwave sounders and collocated high spatial resolution imagers is able to improve the radiance assimilation and TC forecasts. The methodology used in this study can be applied to process data from other pairs of microwave sounders and imagers on board the same platform.
This paper presents an evaluation of the quality of data from the MicroWave Humidity Sounder-2, flown on the FY-3C polar orbiting satellite, and first results of trials assimilating the data in all-sky conditions in the European Centre for Medium Range Weather Forecasts' assimilation system. This instrument combines traditional microwave humidity sounding capabilities at 183 GHz with new channels at 118 GHz, which have never before been available from space. The aim of this paper is twofold: to contribute to the calibration/validation of this satellite by evaluating the data and to test for the first time the impact of assimilating 118-GHz sounding channels in a global numerical weather prediction system. MWHS-2 was first evaluated by comparing observation minus short-range forecast statistics to similar instruments, which indicated that the quality was generally similar. Adding the 118- and 183-GHz channels to the full operational observing system led to small improvements in the short-range (~12 h) forecast accuracy for both sets of channels, and improvements in the 2-4-day wind forecast accuracy for the 183-GHz channels. Short-range forecasts were improved for global humidity in particular when assimilating the 183-GHz channels (0.5% improvement) and for cloud and low-level wind in the Southern Hemisphere extra-tropics when assimilating the 118-GHz channels (by, respectively, 0.5% and 0.2%). In the absence of other atmospheric observations, assimilating the 118-GHz channels improved the global forecast accuracy, but the overall impact was less than for the 183-GHz channels, or for equivalent Advanced Microwave Sounding Unit-A channels.
Output from a high-resolution ensemble data assimilation system is used to assess the ability of an innovative nonlinear bias correction (BC) method that uses a Taylor series polynomial expansion of the observation-minus-background departures to remove linear and nonlinear conditional biases from all-sky satellite infrared brightness temperatures. Univariate and multivariate experiments were performed in which the satellite zenith angle and variables sensitive to clouds and water vapor were used as the BC predictors. The results showed that even though the bias of the entire observation departure distribution is equal to zero regardless of the order of the Taylor series expansion, there are often large conditional biases that vary as a nonlinear function of the BC predictor. The linear first-order term had the largest impact on the entire distribution as measured by reductions in variance; however, large conditional biases often remained in the distribution when plotted as a function of the predictor. These conditional biases were typically reduced to near zero when the nonlinear second- and third-order terms were used. The univariate results showed that variables sensitive to the cloud-top height are effective BC predictors especially when higher-order Taylor series terms are used. Comparison of the statistics for clear-sky and cloudy-sky observations revealed that nonlinear departures are more important for cloudy-sky observations as signified by the much larger impact of the second- and third-order terms on the conditional biases. Together, these results indicate that the nonlinear BC method is able to effectively remove the bias from all-sky infrared observation departures.
This paper investigates changes in the tropical tropopause layer (TTL) in response to carbon dioxide increase and surface warming separately in an atmospheric general circulation model, finding that both effects lead to a warmer tropical tropopause. Surface warming also results in an upward shift of the tropopause. A detailed heat budget analysis is performed to quantify the contributions from different radiative and dynamic processes to changes in the TTL temperature. When carbon dioxide increases with fixed surface temperature, a warmer TTL mainly results from the direct radiative effect of carbon dioxide increase. With surface warming, the largest contribution to the TTL warming comes from the radiative effect of the warmer troposphere, which is partly canceled by the radiative effect of the moistening at the TTL. Strengthening of the stratospheric circulation following surface warming cools the lower stratosphere dynamically and radiatively via changes in ozone. These two effects are of comparable magnitudes. This circulation change is the main cause of temperature changes near 63 hPa but is weak near 100 hPa. Contributions from changes in convection and clouds are also quantified. These results illustrate the heat budget analysis as a useful tool to disentangle the radiative-dynamical-chemical-convective coupling at the TTL and to facilitate an understanding of intermodel difference.
