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Lei Shi

Abstract

Backpropagation neural networks are applied to retrieve atmospheric temperature profiles and tropopause variables from the NOAA-15 Advanced Microwave Sounding Unit-A (AMSU-A) measurement based on two different data sources. The first case uses direct acquisition of 15-channel AMSU-A data over the eastern United States and western Atlantic Ocean for the months of July 1998 and January 1999, and the second case uses recorded global AMSU-A data for several days of January 2000. The corresponding global analysis data from the National Centers for Environmental Prediction are employed to build the neural network training sets. The retrievals yield excellent results in the atmospheric temperature profiles from the surface to the 10-hPa pressure level. For the more generalized global data retrieval case, the root-mean-square (rms) deviation of temperature retrieval is 3.2°C at the surface, only 1.0° to 1.2°C in the midtroposphere, less than 1.5°C around the tropopause, and between 1.0° and 1.5°C in the stratosphere. Simultaneous retrieval of tropopause temperature, height, and pressure yields the rms deviations of 1.9°C, 0.58 km, and 18.1 hPa, respectively, for these variables. Within the scope of regional data, the trained neural network results in smaller values of temperature profile rms deviations than those of the global-data case. When compared to a linear regression approach, the neural network retrieval yields significantly better results for all the atmospheric levels. The neural network with parameters obtained from the network training optimizations can be easily applied to AMSU-A retrieval operationally.

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Eric A. Smith
and
Lei Shi

Abstract

The role of the Tibetan Plateau on the behavior of the surface longwave radiation budget is examined, and the behavior of the vertical profile of longwave cooling over the plateau, including its diurnal variation, is quantified. The investigation has been conducted with the aid of datasets obtained during the 1986 Tibetan Plateau Meteorological Experiment (TIPMEX-86). A medium spectral-resolution infrared radiative transfer model using a simple modification for applications in idealized complex (valley) terrain is developed for the study. This study focuses on the clear-sky case where the surface effects are most significant.

The TIPMEX-86 data, obtained during the spring-summer transition into the East Asian monsoon season, are used to help validate the surface longwave radiation budget at two sites of varying elevation: Lasa (3650 m) and Naqu (4500 m). Based on the degree to which skin-temperature boundary conditions control the magnitude of infrared cooling, we define the concept of relative longwave heating and explain its influence on the vertical infrared cooling-rate profile. Relative longwave radiative heating at the higher-elevation Naqu site is found to be twice as large as that corresponding to the lower-elevation Lasa site located within a valley. Besides reducing the infrared cooling rates, it is shown that relative longwave heating extends the period of the day over which the plateau acts as a direct heat source to the atmosphere. Computational results from the infrared model help substantiate observational analyses that indicate surface longwave net radiation at the high-elevation site, on clear days, exceeds 300 W m−2; this is an order of magnitude greater than typical of sea-level oceanic conditions. As a result of the unique meteorological and surface conditions, total infrared flux convergence occurs within the deep planetary boundary layer (i.e., infrared heating of the cloud-free lower atmosphere) at the high-elevation site during the afternoon. An important characteristic of the daytime longwave heating process of the lower layers is how it turns off like a switch at approximately 1800 MST, transforming almost immediately to maximum cooling of the lower layers.

Atmospheric longwave cooling is significantly influenced by variations in the biophysical composition of the surface and the associated thermal diurnal cycle. It is estimated that natural variations of surface emissivity could modulate longwave cooling by up to 40%. The largest impact would occur at a time when the surface temperature is high and the relative longwave radiative heating of the lower atmosphere by the surface reaches its maximum value.

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Lei Shi
and
Eric A. Smith

Abstract

During the summer east Asian monsoon transition period in 1979, a meteorological field experiment entitled the Qinghai-Xizang Plateau Meteorological Experiment (QXPMEX-79) was conducted over the entire Tibetan Plateau. Data collected on and around the plateau during this period, in conjunction with a medium spectral-resolution infrared radiative transfer model, are used to gain an understanding of how elevation and surface biophysical factors, which are highly variable over the large-scale plateau domain, regulate the spatial distribution of clear-sky infrared cooling during the transition phase of the summer monsoon.

The spatial distribution of longwave cooling over the plateau is significantly influenced by variations in biophysical composition, topography, and elevation, the surface thermal diurnal cycle, and various climatological factors. An important factor is soil moisture. Bulk clear-sky longwave cooling rates are larger in the southeast sector of the plateau than in the north. This is because rainfall is greatest in the southeast, whereas the north is highly desertified and relative longwave radiative heating by the surface is greatest. Another important phenomenon is that the locale of a large-scale east-west-aligned spatial gradient in radiative cooling propagates northward with time. During the premonsoon period (May–June), the location of the strong spatial gradient is found in the southeastern margin of the plateau. Due to changes in surface and atmospheric conditions after the summer monsoon commences, the high gradient sector is shifted to the central Qinghai region. Furthermore, an overall decrease in longwave cooling takes place in the lower atmosphere immediately prior to the arrival of the active monsoon.

The magnitude of longwave cooling is significantly affected by skin-temperature boundary conditions at plateau altitudes. If skin-temperature discontinuities across the surface-atmosphere interface are neglected, bulk cooling rates will be in error up to 1°C day−1. The high surface skin temperatures, particularly in the afternoon, lead to significant relative longwave radiative heating in the lower atmosphere for which the impact in terms of vertical depth is shown to increase rather dramatically as a function of the elevation of the terrain. The significance of these results in the context of previous heat budget studies of the plateau suggest that the radiative heating term (QR ) used by previous investigators contains far too much longwave cooling, and thus in a classic formulation of the Yanai Q 1 balance equation, would lead to underestimation of sensible heating into the atmospheric column.

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Lei Shi
,
Ge Peng
, and
John J. Bates

Abstract

High-latitude ocean surface air temperature and humidity derived from intersatellite-calibrated High-Resolution Infrared Radiation Sounder (HIRS) measurements are examined. A neural network approach is used to develop retrieval algorithms. HIRS simultaneous nadir overpass observations from high latitudes are used to intercalibrate observations from different satellites. Investigation shows that if HIRS observations were not intercalibrated, then it could lead to intersatellite biases of 1°C in the air temperature and 1–2 g kg−1 in the specific humidity for high-latitude ocean surface retrievals. Using a full year of measurements from a high-latitude moored buoy site as ground truth, the instantaneous (matched within a half-hour) root-mean-square (RMS) errors of HIRS retrievals are 1.50°C for air temperature and 0.86 g kg−1 for specific humidity. Compared to a large set of operational moored and drifting buoys in both northern and southern oceans greater than 50° latitude, the retrieval instantaneous RMS errors are within 2.6°C for air temperature and 1.4 g kg−1 for specific humidity. Compared to 5 yr of International Maritime Meteorological Archive in situ data, the HIRS specific humidity retrievals show less than 0.5 g kg−1 of differences over the majority of northern high-latitude open oceans.

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Yonghui Lei
,
Jiancheng Shi
,
Chuan Xiong
, and
Dabin Ji

Abstract

In this study, the net water flux (precipitation minus evaporation) over the Tibetan Plateau (TP) and its 12 drainage basins is estimated using ERA5. The terrestrial branch of the water cycle is investigated using the total water storage anomalies (TWSAs) derived from GRACE (Gravity Recovery and Climate Experiment) data and daily streamflow records collected in Zhimenda and Tangnaihai (two hydrological stations located in the upper Yangtze River Basin and upper Yellow River Basin). This work provides a preliminary assessment of discrepancies between model-derived and space-based observations in the atmospheric–terrestrial water cycle over the TP and its drainage basins. The results show that the net water fluxes occurring over the TP and the scale of its drainage basins are closely tied to local dynamics and physical processes and to large-scale circulation and atmospheric water vapor. ERA5 maintains the atmospheric water balance over the TP. ERA5-derived net water flux anomalies constitute a major component of the water cycle and correspond to GRACE-derived TWSAs. The water budget–based approach with the ERA5 and ITSG-Grace2018 datasets constrains the atmospheric–terrestrial water cycle over the TP and its drainage basins. Both the ERA5- and GRACE-derived estimates contain consistent long- and short-term variations over the TP. Discrepancies are evident at the drainage basin, while the ratio of signal to noise in both the ERA5 and GRACE datasets might cause discrepancies between estimates over relatively small or arid basins. Nevertheless, the observed good correspondence between ERA5- and GRACE-derived atmospheric–terrestrial water cycles over the TP highlights the potential value of the rational application of water resource information.

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Lei Liu
,
Xue-jin Sun
,
Tai-chang Gao
, and
Shi-jun Zhao

Abstract

Cloud properties derived from the whole-sky infrared cloud-measuring system (WSIRCMS) are analyzed in relation to measurements of visual observations and a ceilometer during the period July–August 2010 at the Chinese Meteorological Administration Yangjiang Station, Guangdong Province, China. The comparison focuses on the performance and features of the WSIRCMS as a prototype instrument for automatic cloud observations. Cloud cover derived from the WSIRCMS cloud algorithm compares quite well with cloud cover derived from visual observations. Cloud cover differences between WSIRCMS and visual observations are within ±1 octa in 70.83% and within ±2 octa in 82.44% of the cases. For cloud-base height from WSIRCMS data and Vaisala ceilometer CL51, the comparison shows a generally good correspondence in the lower and midtroposphere up to the height of about 6 km, with some systematic difference due to different detection methods. Differences between the resulting cloud-type classifications derived from the WSIRCMS and from visual observations show that cumulus and cirrus are classified with high accuracy, but that stratocumulus and altocumulus are not. Stratocumulus and altocumulus are suggested to be treated as waveform cloud for classification purposes. In addition, it is considered an intractable problem for automatic cloud-measurement instruments to do cloud classification when the cloud amount is less than 2 octa.

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Yueya Wang
,
Xiaoming Shi
,
Lili Lei
, and
Jimmy Chi-Hung Fung

Abstract

Remote sensing data play a critical role in improving numerical weather prediction (NWP). However, the physical principles of radiation dictate that data voids frequently exist in physical space (e.g., subcloud area for satellite infrared radiance or no-precipitation region for radar reflectivity). Such data gaps impair the accuracy of initial conditions derived from data assimilation (DA), which has a negative impact on NWP. We use the barotropic vorticity equation to demonstrate the potential of deep learning augmented data assimilation (DDA), which involves reconstructing spatially complete pseudo-observation fields from incomplete observations and using them for DA. By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep learning approximation of the “reconstruction operator,” which maps spatially incomplete observations to a model state with full spatial coverage and resolution. The CAE was applied to an incomplete streamfunction observation (∼30% missing) from a high-resolution benchmark simulation and demonstrated satisfactory reconstruction performance, even when only very sparse (1/16 of T63 grid density) observations were used as input. When only spatially incomplete observations are used, the analysis fields obtained from ensemble square root filter (EnSRF) assimilation exhibit significant error. However, in DDA, when EnSRF takes in the combined data from the incomplete observations and CAE reconstruction, analysis error reduces significantly. Such gains are more pronounced with sparse observation and small ensemble size because the DDA analysis is much less sensitive to observation density and ensemble size than the conventional DA analysis, which is based solely on incomplete observations.

Significance Statement

Data assimilation plays a critical role in improving the skills of modern numerical weather prediction by establishing accurate initial conditions. However, unobservable regions are common in observation data, particularly those derived from remote sensing. The nonlinear relationship between data from observable regions and the physical state of unobservable regions may impede DA efficiency. As a result, we propose that deep learning be used to improve data assimilation in such cases by reconstructing a spatially complete first guess of the physical state with deep learning and then applying data assimilation to the reconstructed field. Such deep learning augmentation is found effective in improving the accuracy of data assimilation, especially for sparse observation and small ensemble size.

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Anders V. Lindfors
,
Ian A. Mackenzie
,
Simon F. B. Tett
, and
Lei Shi

Abstract

A climatology of the diurnal cycles of HIRS clear-sky brightness temperatures was developed based on measurements over the period 2002–07. This was done by fitting a Fourier series to monthly gridded brightness temperatures of HIRS channels 1–12. The results show a strong land–sea contrast with stronger diurnal cycles over land, and extending from the surface up to HIRS channel 6 or 5, with regional maxima over the subtropics. Over seas, the diurnal cycles are generally small and therefore challenging to detect. A Monte Carlo uncertainty analysis showed that more robust results are reached by aggregating the data zonally before applying the fit. The zonal fits indicate that small diurnal cycles do exist over sea. The results imply that for a long-lived satellite such as NOAA-14, drift in the overpass time can cause a diurnal sampling bias of more than 5 K for channel 8 (surface and lower troposphere).

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Carl J. Schreck III
,
Lei Shi
,
James P. Kossin
, and
John J. Bates

Abstract

The Madden–Julian oscillation (MJO) and convectively coupled equatorial waves are the dominant modes of synoptic-to-subseasonal variability in the tropics. These systems have frequently been examined with proxies for convection such as outgoing longwave radiation (OLR). However, upper-tropospheric water vapor (UTWV) gives a more complete picture of tropical circulations because it is more sensitive to the drying and warming associated with subsidence. Previous studies examined tropical variability using relatively short (3–7 yr) UTWV datasets. Intersatellite calibration of data from the High Resolution Infrared Radiation Sounder (HIRS) has recently produced a homogeneous 32-yr climate data record of UTWV for 200–500 hPa. This study explores the utility of HIRS UTWV for identifying the MJO and equatorial waves.

Spectral analysis shows that the MJO and equatorial waves stand out above the low-frequency background in UTWV, similar to previous findings with OLR. The fraction of variance associated with the MJO and equatorial Rossby waves is actually greater in UTWV than in OLR. Kelvin waves, on the other hand, are overshadowed in UTWV by horizontal advection from extratropical Rossby waves.

For the MJO, UTWV identifies subsidence drying in the subtropics, poleward of the convection. These dry anomalies are associated with the MJO’s subtropical Rossby gyres. MJO events with dry anomalies over the central North Pacific Ocean also amplify the 200-hPa flow pattern over North America 7 days later. These events cannot be identified using equatorial OLR alone, which demonstrates that UTWV is a useful supplement for identifying the MJO, equatorial waves.

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Ge Peng
,
Lei Shi
,
Steve T. Stegall
,
Jessica L. Matthews
, and
Christopher W. Fairall

Abstract

The accuracy of cloud-screened 2-m air temperatures derived from the intersatellite-calibrated brightness temperatures based on the High Resolution Infrared Radiation Sounder (HIRS) measurements on board the National Oceanic and Atmospheric Administration (NOAA) Polar-Orbiting Operational Environmental Satellite (POES) series is evaluated by comparing HIRS air temperatures to 1-yr quality-controlled measurements collected during the Surface Heat Budget of the Arctic Ocean (SHEBA) project (October 1997–September 1998). The mean error between collocated HIRS and SHEBA 2-m air temperature is found to be on the order of 1°C, with a slight sensitivity to spatial and temporal radii for collocation. The HIRS temperatures capture well the temporal variability of SHEBA temperatures, with cross-correlation coefficients higher than 0.93, all significant at the 99.9% confidence level. More than 87% of SHEBA temperature variance can be explained by linear regression of collocated HIRS temperatures. The analysis found a strong dependency of mean temperature errors on cloud conditions observed during SHEBA, indicating that availability of an accurate cloud mask in the region is essential to further improve the quality of HIRS near-surface air temperature products. This evaluation establishes a baseline of accuracy of HIRS temperature retrievals, providing users with information on uncertainty sources and estimates. It is a first step toward development of a new long-term 2-m air temperature product in the Arctic that utilizes intersatellite-calibrated remote sensing data from the HIRS instrument.

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