scholarly journals Water vapour foreign-continuum absorption in near-infrared windows from laboratory measurements

2012 ◽  
Vol 370 (1968) ◽  
pp. 2557-2577 ◽  
Author(s):  
Igor V. Ptashnik ◽  
Robert A. McPheat ◽  
Keith P. Shine ◽  
Kevin M. Smith ◽  
R. Gary Williams
Keyword(s):  
Water Vapour ◽  
Near Infrared ◽  
Prediction Models ◽  
Weather Prediction ◽  
Pair Interaction ◽  
Atmospheric Conditions ◽  
Laboratory Measurements ◽  
Atmospheric Absorption ◽  
Clear Sky ◽  
Long Time

For a long time, it has been believed that atmospheric absorption of radiation within wavelength regions of relatively high infrared transmittance (so-called ‘windows’) was dominated by the water vapour self-continuum, that is, spectrally smooth absorption caused by H 2 O−H 2 O pair interaction. Absorption due to the foreign continuum (i.e. caused mostly by H 2 O−N 2 bimolecular absorption in the Earth's atmosphere) was considered to be negligible in the windows. We report new retrievals of the water vapour foreign continuum from high-resolution laboratory measurements at temperatures between 350 and 430 K in four near-infrared windows between 1.1 and 5 μm (9000–2000 cm −1 ). Our results indicate that the foreign continuum in these windows has a very weak temperature dependence and is typically between one and two orders of magnitude stronger than that given in representations of the continuum currently used in many climate and weather prediction models. This indicates that absorption owing to the foreign continuum may be comparable to the self-continuum under atmospheric conditions in the investigated windows. The calculated global-average clear-sky atmospheric absorption of solar radiation is increased by approximately 0.46 W m −2 (or 0.6% of the total clear-sky absorption) by using these new measurements when compared with calculations applying the widely used MTCKD (Mlawer–Tobin–Clough–Kneizys–Davies) foreign-continuum model.

scholarly journals Assessment of small-scale integrated water vapour variability during HOPE

2014 ◽  
Vol 14 (16) ◽  
pp. 22837-22879 ◽  
Author(s):  
S. Steinke ◽  
S. Eikenberg ◽  
U. Löhnert ◽  
G. Dick ◽  
D. Klocke ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Water Vapour ◽  
Time Scales ◽  
Near Infrared ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
High Definition ◽  
Integrated Water Vapour ◽  
The Mean

Abstract. The spatio-temporal variability of integrated water vapour (IWV) on small-scales of less than 10 km and hours is assessed with data from the two months of the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE). The statistical intercomparison of the unique set of observations during HOPE (microwave radiometer (MWR), Global Positioning System (GPS), sunphotometer, radiosondes, Raman Lidar, infrared and near infrared Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellites Aqua and Terra) measuring close together reveals a good agreement in terms of standard deviation (≤ 1 kg m−2) and correlation coefficient (≥ 0.98). The exception is MODIS, which appears to suffer from insufficient cloud filtering. For a case study during HOPE featuring a typical boundary layer development, the IWV variability in time and space on scales of less than 10 km and less than 1 h is investigated in detail. For this purpose, the measurements are complemented by simulations with the novel ICOsahedral Non-hydrostatic modelling framework (ICON) which for this study has a horizontal resolution of 156 m. These runs show that differences in space of 3–4 km or time of 10–15 min induce IWV variabilities in the order of 4 kg m−2. This model finding is confirmed by observed time series from two MWRs approximately 3 km apart with a comparable temporal resolution of a few seconds. Standard deviations of IWV derived from MWR measurements reveal a high variability (> 1 kg m−2) even at very short time scales of a few minutes. These cannot be captured by the temporally lower resolved instruments and by operational numerical weather prediction models such as COSMO-DE (an application of the Consortium for Small-scale Modelling covering Germany) of Deutscher Wetterdienst, which is included in the comparison. However, for time scales larger than 1 h, a sampling resolution of 15 min is sufficient to capture the mean standard deviation of IWV. The present study shows that instrument sampling plays a major role when climatological information, in particular the mean diurnal cycle of IWV, is determined.

scholarly journals Assessment of small-scale integrated water vapour variability during HOPE

2015 ◽  
Vol 15 (5) ◽  
pp. 2675-2692 ◽  
Author(s):  
S. Steinke ◽  
S. Eikenberg ◽  
U. Löhnert ◽  
G. Dick ◽  
D. Klocke ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Water Vapour ◽  
Time Scales ◽  
Near Infrared ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
High Definition ◽  
Integrated Water Vapour ◽  
The Mean

Abstract. The spatio-temporal variability of integrated water vapour (IWV) on small scales of less than 10 km and hours is assessed with data from the 2 months of the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE). The statistical intercomparison of the unique set of observations during HOPE (microwave radiometer (MWR), Global Positioning System (GPS), sun photometer, radiosondes, Raman lidar, infrared and near-infrared Moderate Resolution Imaging Spectroradiometer (MODIS) on the satellites Aqua and Terra) measuring close together reveals a good agreement in terms of random differences (standard deviation ≤1 kg m−2) and correlation coefficient (≥ 0.98). The exception is MODIS, which appears to suffer from insufficient cloud filtering. For a case study during HOPE featuring a typical boundary layer development, the IWV variability in time and space on scales of less than 10 km and less than 1 h is investigated in detail. For this purpose, the measurements are complemented by simulations with the novel ICOsahedral Nonhydrostatic modelling framework (ICON), which for this study has a horizontal resolution of 156 m. These runs show that differences in space of 3–4 km or time of 10–15 min induce IWV variabilities on the order of 0.4 kg m−2. This model finding is confirmed by observed time series from two MWRs approximately 3 km apart with a comparable temporal resolution of a few seconds. Standard deviations of IWV derived from MWR measurements reveal a high variability (> 1 kg m−2) even at very short time scales of a few minutes. These cannot be captured by the temporally lower-resolved instruments and by operational numerical weather prediction models such as COSMO-DE (an application of the Consortium for Small-scale Modelling covering Germany) of Deutscher Wetterdienst, which is included in the comparison. However, for time scales larger than 1 h, a sampling resolution of 15 min is sufficient to capture the mean standard deviation of IWV. The present study shows that instrument sampling plays a major role when climatological information, in particular the mean diurnal cycle of IWV, is determined.

scholarly journals Advances in Radiative Transfer Modeling in Support of Satellite Data Assimilation

2007 ◽  
Vol 64 (11) ◽  
pp. 3799-3807 ◽  
Author(s):  
Fuzhong Weng
Keyword(s):  
Radiative Transfer ◽  
Data Assimilation ◽  
Satellite Data ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Conditions ◽  
Clear Sky ◽  
Potential Applications ◽  
Radiative Transfer Modeling ◽  
Global Data

Abstract Development of fast and accurate radiative transfer models for clear atmospheric conditions has enabled direct assimilation of clear-sky radiances from satellites in numerical weather prediction models. In this article, fast radiative transfer schemes and their components critical for satellite data assimilation are summarized and discussed for their potential applications in operational global data assimilation systems. The major impediments to the fast radiative transfer schemes are highlighted and a call is made for broader community efforts to develop advanced radiative transfer components that can better handle the scattering from atmospheric constituents (e.g., aerosols, clouds, and precipitation) and surface materials (e.g., snow, sea ice, deserts).

scholarly journals Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles

2017 ◽  
Vol 32 (5) ◽  
pp. 1819-1840 ◽  
Author(s):  
David John Gagne ◽  
Amy McGovern ◽  
Sue Ellen Haupt ◽  
Ryan A. Sobash ◽  
John K. Williams ◽  
...  
Keyword(s):  
Machine Learning ◽  
Size Distribution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Radar Data ◽  
Object Identification ◽  
Atmospheric Conditions ◽  
Learning Models ◽  
Probabilistic Machine Learning ◽  
Machine Learning Models

Abstract Forecasting severe hail accurately requires predicting how well atmospheric conditions support the development of thunderstorms, the growth of large hail, and the minimal loss of hail mass to melting before reaching the surface. Existing hail forecasting techniques incorporate information about these processes from proximity soundings and numerical weather prediction models, but they make many simplifying assumptions, are sensitive to differences in numerical model configuration, and are often not calibrated to observations. In this paper a storm-based probabilistic machine learning hail forecasting method is developed to overcome the deficiencies of existing methods. An object identification and tracking algorithm locates potential hailstorms in convection-allowing model output and gridded radar data. Forecast storms are matched with observed storms to determine hail occurrence and the parameters of the radar-estimated hail size distribution. The database of forecast storms contains information about storm properties and the conditions of the prestorm environment. Machine learning models are used to synthesize that information to predict the probability of a storm producing hail and the radar-estimated hail size distribution parameters for each forecast storm. Forecasts from the machine learning models are produced using two convection-allowing ensemble systems and the results are compared to other hail forecasting methods. The machine learning forecasts have a higher critical success index (CSI) at most probability thresholds and greater reliability for predicting both severe and significant hail.

scholarly journals Effects of improvements in near-infrared water vapour line intensities on short-wave atmospheric absorption

2001 ◽  
Vol 28 (12) ◽  
pp. 2401-2404
Author(s):  
Júlio C. S. Chagas ◽  
David A. Newnham ◽  
Kevin M. Smith ◽  
Keith P. Shine
Keyword(s):  
Water Vapour ◽  
Near Infrared ◽  
Short Wave ◽  
Atmospheric Absorption ◽  
Line Intensities

scholarly journals Atmospheric observations of the water vapour continuum in the near-infrared windows between 2500–6600 cm<sup>−1</sup>

2019 ◽  
Author(s):  
Jonathan Elsey ◽  
Marc D. Coleman ◽  
Tom D. Gardiner ◽  
Kaah P. Menang ◽  
Keith P. Shine
Keyword(s):  
Water Vapour ◽  
Near Infrared ◽  
Laboratory Data ◽  
Field Measurements ◽  
The Self ◽  
Atmospheric Conditions ◽  
Sensing Applications ◽  
Atmospheric Observations ◽  
Real Atmosphere ◽  
Remote Sensing Applications

Abstract. Water vapour continuum absorption is potentially important for both closure of the Earth’s energy budget and remote sensing applications. Currently, there are significant uncertainties in its characteristics in the near-infrared atmospheric windows at 2.1 and 1.6 μm. There have been several attempts to measure the continuum in the laboratory; not only are there significant differences amongst these measurements but there are also difficulties in extrapolating the laboratory data taken at room temperature or higher to atmospheric temperatures. Validation is therefore required using field observations of the real atmosphere. There are currently few published observations in atmospheric conditions with enough water vapour to detect a continuum signal within these windows, or where the self-continuum component is significant. We present observations of the near-infrared water vapour continuum from Camborne, UK at sea level using a sun-pointing, radiometrically-calibrated Fourier transform spectrometer in the window regions between 2000–10000 cm−1. Analysis of this data is challenging, particularly because of the need to remove aerosol extinction, and the large uncertainties associated with such field measurements. Nevertheless, we present data that is consistent with recent laboratory datasets in the 4 and 2.1 μm windows (when extrapolated to atmospheric temperatures). These results indicate that the most recent revision (3.2) of the MT_CKD foreign continuum, versions of which are widely used in atmospheric radiation models, requires strengthening by a factor of ~ 5 in the centre of the 2.1 µm window. In the higher-wavenumber window at 1.6 µm, our estimated self and foreign continua are significantly stronger than MT_CKD. The possible contribution of the self and foreign continua to our derived total continuum optical depth is estimated by using laboratory or MT_CKD values of one, to estimate the other. The obtained self-continuum shows some consistency with temperature-extrapolated laboratory data in the centres of the 4 and 2.1 µm windows. The 1.6 μm region is more sensitive to atmospheric aerosol and continuum retrievals and therefore more uncertain than the more robust results at 2.1 and 4 μm. We highlight the difficulties in observing the atmospheric continuum and make the case for additional measurements from both the laboratory and field, with discussion of the requirements for any new field campaign.

scholarly journals Quality assessment of integrated water vapour measurements at the St. Petersburg site, Russia: FTIR vs. MW and GPS techniques

2017 ◽  
Vol 10 (11) ◽  
pp. 4521-4536 ◽  
Author(s):  
Yana A. Virolainen ◽  
Yury M. Timofeyev ◽  
Vladimir S. Kostsov ◽  
Dmitry V. Ionov ◽  
Vladislav V. Kalinnikov ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Water Vapour ◽  
Measurement Errors ◽  
Microwave Radiometer ◽  
Reference Method ◽  
Atmospheric Conditions ◽  
Assessment Protocol ◽  
Clear Sky ◽  
Integrated Water Vapour ◽  
Sky Conditions

Abstract. The cross-comparison of different techniques for atmospheric integrated water vapour (IWV) measurements is the essential part of their quality assessment protocol. We inter-compare the synchronised data sets of IWV values measured by the Bruker 125 HR Fourier-transform infrared spectrometer (FTIR), RPG-HATPRO microwave radiometer (MW), and Novatel ProPak-V3 global navigation satellite system receiver (GPS) at the St. Petersburg site between August 2014 and October 2016. As the result of accurate spatial and temporal matching of different IWV measurements, all three techniques agree well with each other except for small IWV values. We show that GPS and MW data quality depends on the atmospheric conditions; in dry atmosphere (IWV smaller than 6 mm), these techniques are less reliable at the St. Petersburg site than the FTIR method. We evaluate the upper bound of statistical measurement errors for clear-sky conditions as 0.29 ± 0.02 mm (1.6 ± 0.3 %), 0.55 ± 0.02 mm (4.7 ± 0.4 %), and 0.76 ± 0.04 mm (6.3 ± 0.8 %) for FTIR, GPS, and MW methods, respectively. We propose the use of FTIR as a reference method under clear-sky conditions since it is reliable on all scales of IWV variability.

Benefits of the Advanced Baseline Imager (ABI) for Ensemble-Based Analysis and Prediction of Severe Thunderstorms

2020 ◽  
Author(s):  
Yunji Zhang ◽  
David J. Stensrud ◽  
Eugene E. Clothiaux
Keyword(s):  
Prediction Models ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Sampling Errors ◽  
Severe Thunderstorm ◽  
Clear Sky ◽  
Severe Thunderstorms ◽  
Challenges And Opportunities ◽  
Open Question ◽  
Numerical Weather Prediction Models

AbstractRecent studies have demonstrated advances in the analysis and prediction of severe thunderstorms and other weather hazards by assimilating infrared (IR) all-sky radiances into numerical weather prediction models using advanced ensemble-based techniques. It remains an open question how many of these advances are due to improvements in the radiance observations themselves, especially when compared with radiance observations from preceding satellite imagers. This study investigates the improvements gained by assimilation of IR all-sky radiances from the Advanced Baseline Imager (ABI) onboard the GOES-16 satellite compared to those from its predecessor imager. Results show that all aspects of the improvements in ABI compared with its predecessor imager – finer spatial resolution, shorter scanning intervals, and more channels covering a wider range of the spectrum – contribute to more accurate ensemble analyses and forecasts of the targeted severe thunderstorm event, but in different ways. The clear-sky regions within the assimilated all-sky radiance fields have a particularly beneficial influence on the moisture fields. Results also show that assimilating different IR channels can lead to oppositely signed increments in the moisture fields, a byproduct of inaccurate covariances at large distances resulting from sampling errors. These findings pose both challenges and opportunities in identifying appropriate vertical localizations and IR channel combinations to produce the best possible analyses in support of severe weather forecasting.

Structure of weather prediction errors in stably-stratified atmospheric conditions

2020 ◽  
Author(s):  
Igor Esau ◽  
Stephen Outten ◽  
Mikhail Tolstykh
Keyword(s):  
Surface Layer ◽  
Mixed Layer ◽  
Turbulent Mixing ◽  
Prediction Models ◽  
Numerical Models ◽  
Temporal Structure ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Prediction Errors ◽  
Atmospheric Conditions

&lt;p&gt;Stably-stratified atmospheric conditions still challenge numerical weather forecast, especially in high latitudes where they are frequently observed all year around. In stably-stratified atmosphere, surface is colder than air above. Such conditions suppress vertical turbulent mixing and may lead to surface layer decoupling in numerical models. Enhanced mixing could prevent decoupling but being implemented without sufficient care results in damped response of the surface layer meteorological variables on fluctuations of the weather conditions. In this study, we investigate weather prediction errors related to such a damped response. We run a group of operational prediction models (HIRLAM-HARMONIE, SL-AV) with a set of different turbulence parametrizations that includes HARATU, TOUCANS, and pTKE schemes. The results are compared with real weather observations and idealized GABLS setups proposed for a high latitude domain. We found that the systematic warm temperature bias in the models is caused by too slow response of the modelled temperature on the implied cooling. The largest (and quickly growing) errors are found over the first few hours of cooling, whereas in longer perspective the errors diminish as the model equilibrates with more stationary weather conditions. We develop a theory that may explain the observed structure of weather prediction errors. The explanation is based on the well-known coupling between the turbulent mixing intensity and the thickness of the mixed layer embedded into the parametrization of the mixing length scale. The required enhanced mixing could be provided by the energy-flux balance scheme by Zilitinkevich et al., but it does not reduce the warm bias as it makes the mixed deeper and less responsive. We propose more accurate limitations on the mixed layer thickness to improve the temporal structure of the surface layer temperature response in the weather prediction models.&lt;/p&gt;

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