Maintenance of Lower Tropospheric Temperature Inversion in the Saharan Air Layer by Dust and Dry Anomaly

2009 ◽  
Vol 22 (19) ◽  
pp. 5149-5162 ◽  
Author(s):  
Sun Wong ◽  
Andrew E. Dessler ◽  
Natalie M. Mahowald ◽  
Ping Yang ◽  
Qian Feng
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Heating Rate ◽  
Temperature Inversion ◽  
Dust Content ◽  
Radiative Heating ◽  
Specific Humidity ◽  
Transfer Model ◽  
Tropospheric Temperature ◽  
Saharan Air Layer

Abstract The role of Saharan dust and dry anomaly in maintaining the temperature inversion in the Saharan air layer (SAL) is investigated. The dust aerosol optical thickness (AOT) in the SAL is inferred from the measurements taken by Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the corresponding temperature and specific humidity anomalies are identified using the National Centers for Environmental Prediction (NCEP) data in August–September over the North Atlantic tropical cyclone (TC) main development region (MDR; 10°–20°N, 40°–60°W). The authors also study the SAL simulated in the National Center of Atmospheric Research (NCAR) Community Atmosphere Model, version 3 (CAM3), coupled with dust radiative effect. It is found that higher AOT is associated with warmer and dryer anomalies below 700 hPa, which increases the atmospheric stability. The calculated instantaneous radiative heating anomalies from a radiative transfer model indicate that both the dust and low humidity are essential to maintaining the temperature structure in the SAL against thermal relaxation. At 850 hPa, heating anomalies caused by both the dust and dry anomalies (for AOT > 0.8) are 0.2–0.4 K day−1. The dust heats the atmosphere below 600 hPa, while the dry anomaly cools the atmosphere below 925 hPa, resulting in a peak of heating rate anomaly located at 700–850 hPa. In the eastern Atlantic, dust contributes about 50% of the heating rate anomaly. Westward of 40°W, when the dust content becomes small (AOT < 0.6), the heating rates are more sensitive to the water vapor profile used in the radiative transfer calculation. Retrieving or simulating correct water vapor profiles is essential to the assessment of the SAL heating budgets in regions where the dust content in the SAL is small.

Using deep learning to emulate and accelerate a radiative-transfer model

2021 ◽  
Author(s):  
Ryan Lagerquist ◽  
David Turner ◽  
Imme Ebert-Uphoff ◽  
Jebb Stewart ◽  
Venita Hagerty
Keyword(s):  
Deep Learning ◽  
Radiative Transfer ◽  
Heating Rate ◽  
Weather Prediction ◽  
Computing Time ◽  
Single Layer ◽  
Radiative Heating ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Heating Rates

AbstractThis paper describes the development of U-net++ models, a type of neural network that performs deep learning, to emulate the shortwave Rapid Radiative-transfer Model (RRTM). The goal is to emulate the RRTM accurately in a small fraction of the computing time, creating a U-net++ that could be used as a parameterization in numerical weather prediction (NWP). Target variables are surface downwelling flux, top-of-atmosphere upwelling flux (), net flux, and a profile of radiative-heating rates. We have devised several ways to make the U-net++ models knowledge-guided, recently identified as a key priority in machine learning (ML) applications to the geosciences. We conduct two experiments to find the best U-net++ configurations. In Experiment 1, we train on non-tropical sites and test on tropical sites, to assess extreme spatial generalization. In Experiment 2, we train on sites from all regions and test on different sites from all regions, with the goal of creating the best possible model for use in NWP. The selected model from Experiment 1 shows impressive skill on the tropical testing sites, except four notable deficiencies: large bias and error for heating rate in the upper stratosphere, unreliable for profiles with single-layer liquid cloud, large heating-rate bias in the mid-troposphere for profiles with multi-layer liquid cloud, and negative bias at lowzenith angles for all flux components and tropospheric heating rates. The selected model from Experiment 2 corrects all but the first deficiency, and both models run ~104 times faster than the RRTM. Our code is available publicly.

scholarly journals Long-Term Observations of Microwave Brightness Temperatures over a Metropolitan Area: Comparison of Radiometric Data and Spectra Simulated with the Use of Radiosonde Measurements

2021 ◽  
Vol 13 (11) ◽  
pp. 2061
Author(s):  
Mikhail V. Belikovich ◽  
Mikhail Yu. Kulikov ◽  
Dmitry S. Makarov ◽  
Natalya K. Skalyga ◽  
Vitaly G. Ryskin ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Elevation Angle ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Systematic Bias ◽  
Tropospheric Temperature ◽  
Absorption Model ◽  
60 Ghz ◽  
Error Budget ◽  
The Difference

Ground-based microwave radiometers are increasingly used in operational meteorology and nowcasting. These instruments continuously measure the spectra of downwelling atmospheric radiation in the range 20–60 GHz used for the retrieval of tropospheric temperature and water vapor profiles. Spectroscopic uncertainty is an important part of the retrieval error budget, as it leads to systematic bias. In this study, we analyze the difference between observed and simulated microwave spectra obtained from more than four years of microwave and radiosonde observations over Nizhny Novgorod (56.2° N, 44° E). We focus on zenith-measured and elevation-scanning data in clear-sky conditions. The simulated spectra are calculated by a radiative transfer model with the use of radiosonde profiles and different absorption models, corresponding to the latest spectroscopy research. In the case of zenith-measurements, we found a systematic bias (up to ~2 K) of simulated spectra at 51–54 GHz. The sign of bias depends on the absorption model. A thorough investigation of the error budget points to a spectroscopic nature of the observed differences. The dependence of the results on the elevation angle and absorption model can be explained by the basic properties of radiative transfer and by cloud contamination at elevation angles.

scholarly journals Are elevated moist layers a blind spot for hyperspectral infrared sounders? A model study

2021 ◽  
Vol 14 (11) ◽  
pp. 7025-7044
Author(s):  
Marc Prange ◽  
Manfred Brath ◽  
Stefan A. Buehler
Keyword(s):  
Heating Rate ◽  
Local Heating ◽  
Optimal Estimation ◽  
Forecast Model ◽  
Temperature Inversion ◽  
Radiative Heating ◽  
Reference Dataset ◽  
Tropical Ocean ◽  
Systematic Analysis ◽  
New Framework

Abstract. The ability of the hyperspectral satellite-based passive infrared (IR) instrument IASI to resolve elevated moist layers (EMLs) within the free troposphere is investigated. EMLs are strong moisture anomalies with significant impact on the radiative heating rate profile and typically coupled to freezing level detrainment from convective cells in the tropics. A previous case study by Stevens et al. (2017) indicated inherent deficiencies of passive satellite-based remote sensing instruments in resolving an EML. In this work, we first put the findings of Stevens et al. (2017) into the context of other retrieval case studies of EML-like structures, showing that such structures can in principle be retrieved, but retrievability depends on the retrieval method and the exact retrieval setup. To approach a first more systematic analysis of EML retrievability, we introduce our own basic optimal estimation (OEM) retrieval, which for the purpose of this study is based on forward-modelled (synthetic) clear-sky observations. By applying the OEM retrieval to the same EML case as Stevens et al. (2017), we find that a lack of independent temperature information can significantly deteriorate the humidity retrieval due to a strong temperature inversion at the EML top. However, we show that by employing a wider spectral range of the hyperspectral IR observation, this issue can be avoided and EMLs can generally be resolved. We introduce a new framework for the identification and characterization of moisture anomalies, a subset of which are EMLs, to specifically quantify the retrieval's ability to capture moisture anomalies. The new framework is applied to 1288 synthetic retrievals of tropical ocean short-range forecast model atmospheres, allowing for a direct statistical comparison of moisture anomalies between the retrieval and the reference dataset. With our basic OEM retrieval, we find that retrieved moisture anomalies are on average 17 % weaker and 15 % thicker than their true counterparts. We attribute this to the retrieval smoothing error and the fact that rather weak and narrow moisture anomalies are most frequently missed by the retrieval. Smoothing is found to also constrain the magnitude of local heating rate extremes associated with moisture anomalies, particularly for the strongest anomalies that are found in the lower to mid troposphere. In total, about 80 % of moisture anomalies in the reference dataset are found by the retrieval. Below 5 km altitude, this fraction is only of the order of 52 %. We conclude that the retrieval of lower- to mid-tropospheric moisture anomalies, in particular of EMLs, is possible when the anomaly is sufficiently strong and its thickness is at least of the order of about 1.5 km. This study sets the methodological basis for more comprehensively investigating EMLs based on real hyperspectral IR observations and their operational products in the future.

scholarly journals Performance of the line-by-line radiative transfer model (LBLRTM) for temperature and species retrievals: IASI case studies from JAIVEx

2009 ◽  
Vol 9 (19) ◽  
pp. 7397-7417 ◽  
Author(s):  
M. W. Shephard ◽  
S. A. Clough ◽  
V. H. Payne ◽  
W. L. Smith ◽  
S. Kireev ◽  
...  
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Line Intensities ◽  
Remote Sounding ◽  
Instrument Noise ◽  
Large Peak ◽  
Line Coupling

Abstract. Presented here are comparisons between the Infrared Atmospheric Sounding instrument (IASI) and the "Line-By-Line Radiative Transfer Model" (LBLRTM). Spectral residuals from radiance closure studies during the IASI JAIVEx validation campaign provide insight into a number of spectroscopy issues relevant to remote sounding of temperature, water vapor and trace gases from IASI. In order to perform quality IASI trace gas retrievals, the temperature and water vapor fields must be retrieved as accurately as possible. In general, the residuals in the CO2 ν2 region are of the order of the IASI instrument noise. However, outstanding issues with the CO2 spectral regions remain. There is a large residual ~−1.7 K in the 667 cm−1 Q-branch, and residuals in the CO2 ν2 and N2O/CO2 ν3 spectral regions that sample the troposphere are inconsistent, with the N2O/CO2 ν3 region being too negative (warmer) by ~0.7 K. Residuals on this lower wavenumber side of the CO2 ν3 band will be improved by line parameter updates, while future efforts to reduce the residuals reaching ~−0.5 K on the higher wavenumber side of the CO2 ν3 band will focus on addressing limitations in the modeling of the CO2 line shape (line coupling and duration of collision) effects. Brightness temperature residuals from the radiance closure studies in the ν2 water vapor band have standard deviations of ~0.2–0.3 K with some large peak residuals reaching ±0.5–1.0 K. These are larger than the instrument noise indicating that systematic errors still remain. New H2O line intensities and positions have a significant impact on the retrieved water vapor, particularly in the upper troposphere where the water vapor retrievals are 10% drier when using line intensities compared with HITRAN 2004. In addition to O3, CH4, and CO, of the IASI instrument combined with an accurate forward model allows for the detection of minor species with weak atmospheric signatures in the nadir radiances, such as HNO3 and OCS.

scholarly journals Impacts of Water Vapor on Saharan Air Layer Radiative Heating

2019 ◽  
Vol 46 (24) ◽  
pp. 14854-14862 ◽  
Author(s):  
Manuel Gutleben ◽  
Silke Groß ◽  
Martin Wirth ◽  
Claudia Emde ◽  
Bernhard Mayer
Keyword(s):  
Water Vapor ◽  
Radiative Heating ◽  
Saharan Air Layer

Greenhouse effects of aircraft emissions as calculated by a radiative transfer model

1995 ◽  
Vol 13 (4) ◽  
pp. 413-418 ◽  
Author(s):  
J. P. F. Fortuin ◽  
R. van Dorland ◽  
W. M. F. Wauben ◽  
H. Kelder
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Indirect Effect ◽  
Radiative Forcing ◽  
Combustion Products ◽  
Ozone Production ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Aircraft Emissions ◽  
The North

Abstract. With a radiative transfer model, assessments are made of the radiative forcing in northern mid-latitudes due to aircraft emissions up to 1990. Considered are the direct climate effects from the major combustion products carbon dioxide, nitrogen dioxide, water vapor and sulphur dioxide, as well as the indirect effect of ozone production from NOx emissions. Our study indicates a local radiative forcing at the tropopause which should be negative in summer (–0.5 to 0.0 W/m2) and either negative or positive in winter (–0.3 to 0.2 W/m2). To these values the indirect effect of contrails has to be added, which for the North Atlantic Flight Corridor covers the range –0.2 to 0.3 W/m2 in summer and 0.0 to 0.3 W/m2 in winter. Apart from optically dense non-aged contrails during summer, negative forcings are due to solar screening by sulphate aerosols. The major positive contributions come from contrails, stratospheric water vapor in winter and ozone in summer. The direct effect of NO2 is negligible and the contribution of CO2 is relatively small.

scholarly journals Performance of the Line-By-Line Radiative Transfer Model (LBLRTM) for temperature, water vapor, and trace gas retrievals: recent updates evaluated with IASI case studies

2013 ◽  
Vol 13 (14) ◽  
pp. 6687-6711 ◽  
Author(s):  
M. J. Alvarado ◽  
V. H. Payne ◽  
E. J. Mlawer ◽  
G. Uymin ◽  
M. W. Shephard ◽  
...  
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Fixed Ratio ◽  
Satellite Observations ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Temperature Profiles ◽  
A Posteriori ◽  
The Impact ◽  
Temperature Water

Abstract. Modern data assimilation algorithms depend on accurate infrared spectroscopy in order to make use of the information related to temperature, water vapor (H2O), and other trace gases provided by satellite observations. Reducing the uncertainties in our knowledge of spectroscopic line parameters and continuum absorption is thus important to improve the application of satellite data to weather forecasting. Here we present the results of a rigorous validation of spectroscopic updates to an advanced radiative transfer model, the Line-By-Line Radiative Transfer Model (LBLRTM), against a global dataset of 120 near-nadir, over-ocean, nighttime spectra from the Infrared Atmospheric Sounding Interferometer (IASI). We compare calculations from the latest version of LBLRTM (v12.1) to those from a previous version (v9.4+) to determine the impact of spectroscopic updates to the model on spectral residuals as well as retrieved temperature and H2O profiles. We show that the spectroscopy in the CO2 ν2 and ν3 bands is significantly improved in LBLRTM v12.1 relative to v9.4+, and that these spectroscopic updates lead to mean changes of ~0.5 K in the retrieved vertical temperature profiles between the surface and 10 hPa, with the sign of the change and the variability among cases depending on altitude. We also find that temperature retrievals using each of these two CO2 bands are remarkably consistent in LBLRTM v12.1, potentially allowing these bands to be used to retrieve atmospheric temperature simultaneously. The updated H2O spectroscopy in LBLRTM v12.1 substantially improves the a posteriori residuals in the P-branch of the H2O ν2 band, while the improvements in the R-branch are more modest. The H2O amounts retrieved with LBLRTM v12.1 are on average 14% lower between 100 and 200 hPa, 42% higher near 562 hPa, and 31% higher near the surface compared to the amounts retrieved with v9.4+ due to a combination of the different retrieved temperature profiles and the updated H2O spectroscopy. We also find that the use of a fixed ratio of HDO to H2O in LBLRTM may be responsible for a significant fraction of the remaining bias in the P-branch relative to the R-branch of the H2O ν2 band. There were no changes to O3 spectroscopy between the two model versions, and so both versions give positive a posteriori residuals of ~ 0.3 K in the R-branch of the O3 ν3 band. While the updates to the H2O self-continuum employed by LBLRTM v12.1 have clearly improved the match with observations near the CO2 ν3 band head, we find that these updates have significantly degraded the match with observations in the fundamental band of CO. Finally, significant systematic a posteriori residuals remain in the ν4 band of CH4, but the magnitude of the positive bias in the retrieved mixing ratios is reduced in LBLRTM v12.1, suggesting that the updated spectroscopy could improve retrievals of CH4 from satellite observations.

scholarly journals Improving Satellite-Derived Sea Surface Temperature Accuracies Using Water Vapor Profile Data

2011 ◽  
Vol 28 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Ian J. Barton
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
Sea Surface ◽  
Radiative Transfer Model ◽  
Clear Correlation ◽  
Transfer Model ◽  
The Difference

Abstract Analyses based on atmospheric infrared radiative transfer simulations and collocated ship and satellite data are used to investigate whether knowledge of vertical atmospheric water vapor distributions can improve the accuracy of sea surface temperature (SST) estimates from satellite data. Initially, a simulated set of satellite brightness temperatures generated by a radiative transfer model with a large maritime radiosonde database was obtained. Simple linear SST algorithms are derived from this dataset, and these are then reapplied to the data to give simulated SST estimates and errors. The concept of water vapor weights is introduced in which a weight is a measure of the layer contribution to the difference between the surface temperature and that measured by the satellite. The weight of each atmospheric layer is defined as the layer water vapor amount multiplied by the difference between the SST and the midlayer temperature. Satellite-derived SST errors are then plotted against the difference in the sum of weights above an altitude of 2.5 km and that below. For the simple two-channel (with typical wavelengths of 11 and 12 μm) analysis, a clear correlation between the weights differences and the SST errors is found. A second group of analyses using ship-released radiosondes and satellite data also show a correlation between the SST errors and the weights differences. The analyses suggest that, for an SST derived using a simple two-channel algorithm, the accuracy may be improved if account is taken of the vertical distribution of water vapor above the ocean surface. For SST estimates derived using algorithms that include data from a 3.7-μm channel, there is no such correlation found.

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