Total Water Vapor Research Articles

A New Bottom-of-Atmosphere (BOA) Radiance-Based Hybrid Method for Estimating Clear-Sky Surface Longwave Upwelling Radiation From MODIS Data

Surface longwave upwelling radiation (SLUR) is a critical parameter for studying the water-energy balance between the land surface and atmosphere. In this study, we proposed a new hybrid method for estimating the clear-sky SLUR from the bottom-of-atmosphere (BOA) radiance (new BOA-hybrid method) of the Moderate Resolution Imaging Spectroradiometer (MODIS). There are two key parts in the developed new BOA-hybrid method. First, a physical four-channel algorithm was developed to estimate the atmospheric terms (atmospheric upwelling radiance and transmittance), which were used to calculate MODIS BOA radiance. Second, a linear model linking SLUR and MODIS BOA channel radiances was constructed using a large number of samples generated by extensive radiative transfer simulations. In situ measurements from twenty-seven sites in four flux networks were collected to validate the new BOA-hybrid method. The average bias and RMSE were 1.65 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 16.23 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the new BOA-hybrid method, which was superior to the original BOA-hybrid method whose bias and RMSE were 3.52 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 18.51 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and slightly better than the classical hybrid method that estimates SLUR using the top-of-atmosphere (TOA) radiance (TOA-hybrid method) whose bias and RMSE were -1.64 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 17.44 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The performance of the new and original BOA-hybrid methods was similar when the total precipitable water vapor (TPW) was less than 1.5 cm. However, under a large TPW (i.e., TPW>3 cm), especially in the case of a large viewing zenith angle (i.e., VZA>45°), the accuracy of the original BOA-hybrid method decreased significantly, while the physical four-channel algorithm could effectively improve the accuracy of atmospheric correction and the RMSE of the SLUR estimated by BOA-hybrid could be reduced by more than 10 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . In summary, the developed new BOA-hybrid method can estimate SLUR accurately and has great potential to produce a long-term high spatial resolution environmental data record of SLUR.

Estimating Hourly Land Surface Temperature From FY-4A AGRI Using an Explicitly Emissivity-Dependent Split-Window Algorithm

Land surface emissivity (LSE) has been roughly treated in the current split-window (SW) LST retrieval algorithms. This paper extended the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Enterprise algorithm to Feng Yun-4A (FY-4A)/Advanced Geostationary Radiation Imager (AGRI) thermal infrared (TIR) data by incorporating a daily LSE database for high-temporal resolution LST retrieval. To improve the retrieval accuracy, the day/night split-window (SW) algorithm coefficients were calculated for different total water vapor content and view zenith angle conditions using the simulation database constructed by MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles. The validation results show that the daily AGRI LSE has better accuracy than the LSE retrieved from the vegetation cover method (VCM), with average biases of -1.1×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> and -6×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> for channels 12 and 13. The accuracy of the AGRI LST retrieved using the daily AGRI LSE is slightly better than that retrieved using the VCM-retrieved LSE. The overall bias, MAE, and RMSE of the retrieved AGRI LST using the daily AGRI LSE at fourteen in situ sites are 0.11, 2.55, and 2.55 K, whereas these values are -0.11, 2.70 and 2.70 K for the LST using the VCM-retrieved LSE. This study demonstrates the daily LSE constructed from physically retrieved LSE can improve the accuracy of LST retrieved with the SW algorithm. The constructed daily LSE has high spatial coverage and dynamic emissivity information and can provide nearly complete spatial coverage if supplemented by the constructed 8-day or monthly AGRI LSE. It can also be applied to other LST retrieval algorithms that need LSE as a priori.

Horizontal small-scale variability of water vapor in the atmosphere: implications for intercomparison of data from different measuring systems

Abstract. Water vapor concentration structures in the atmosphere are well approximated horizontally by Gaussian random fields at small scales (≲6 km). These Gaussian random fields have a spatial correlation in accordance with a structure function with a two-thirds slope, following the corresponding law from Kolmogorov's theory of turbulence. This is proven by showing that the horizontal structure functions measured by several satellite instruments and radiosonde measurements do indeed follow the two-thirds law. High-spatial-resolution retrievals of total column water vapor (TCWV) obtained from the Ocean and Land Color Instrument (OLCI) on board the Sentinel-3 series of satellites also qualitatively show a Gaussian random field structure. As a consequence, the atmosphere has an inherently stochastic component associated with the horizontal small-scale water vapor features, which, in turn, can make deterministic forecasting or nowcasting difficult. These results can be useful in areas where high-resolution modeling of water vapor is required, such as the estimation of the water vapor variance within a region or when searching for consistency between different water vapor measurements in neighboring locations. In terms of weather forecasting or nowcasting, the water vapor horizontal variability could be important in estimating the uncertainty of the atmospheric processes driving convection.

New Models for Vertical Distribution and Variation of Tropospheric Water Vapor—A Case Study for China

For better modeling the vertical distribution and variations of water vapor, a 10-year time-series of a newly derived water vapor parameter (termed IRPWV), defined as the ratio of water vapor density (WVD) to the total precipitable water vapor (TPWV), was statistically analyzed. This research showed that the vertical distribution of IRPWV presents a periodic pattern and is highly correlated with the relative magnitude of its corresponding TPWV when compared with the other TPWVs in the same time range. Six TPWV ranges were first chosen to determine the relative magnitude and then used to classify the IRPWV vertical distributions of TPWV. For the periodic variations in each of the six classified IRPWV vertical distribution time-series, a temporal IRPWV model was developed accordingly with six sets of coefficients. The new models were validated by comparing their predictions against the reference values from sounding data at 12 radiosonde stations in China, and their performance was also evaluated against that of the commonly used exponential model. Results showed that, first, the proportions of the height range that had reduced annual root mean square error (RMSE) of WVD in all height ranges within all TPWV ranges were over 75% at the 12 stations. Then, the annual RMSEs of the WVD for all the stations were reduced by at least 11%, 20%, 43%, 48%, 40%, 38%, 32%, 35%, 32%, and 28% in each of the 10 selected height ranges, respectively.

Global precipitation system scale increased from 2001 to 2020

How precipitation responds to global warming has drawn much interest from the scientific community. However, as one of the important properties of precipitation, changes in the precipitation system scale have rarely been studied. The latest retrospective merged satellite precipitation product has provided a great opportunity to investigate quasi-global precipitation system scale changes. This study uses the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) data during 2001–2020 to investigate quasi-global precipitation system scale changes. Our results indicate that, generally, quasi-global precipitation system scales exhibit a significant increasing trend, which is more significant in winter and spring than that in summer and autumn over both hemispheres. The scale of nearly all precipitation systems over land regions has increased over the last two decades. In oceanic regions, precipitation system scales increase over tropical oceans but decrease over subtropical oceans. Medium-scale precipitation systems (100–1000 km) significantly increase in scale and decrease in frequency, while large-scale precipitation systems significantly increase in frequency, which both lead to a quasi-global increase in the precipitation system scale. Diagnostic investigation based on atmospheric analysis reveals that an increase in atmospheric stability tends to suppress local convective precipitation, while an increase in the total column water vapor could help maintain and enhance precipitation system scale, which jointly contribute to quasi-global precipitation system scale increase.

Eddy covariance measurement-based differences in annual evapotranspiration between forests and grasslands in China.

Annual evapotranspiration (AET), the total water vapor loss to the atmosphere during a year, is a vital process of global water cycles and energy cycles. Revealing the differences in AET values and spatial variations between forests and grasslands would benefit for understanding AET spatial variations, which serves as a basis for regional water management. Based on published eddy covariance measurements in China, we collected AET values from 29 forests and 46 grasslands, and analyzed the differences in AET values and spatial variations between forests and grasslands in China. The results showed that forests had a significant higher AET (645.98 ± 232.73 kgH2O m-2 yr-1) than grasslands (359.31 ± 156.02 kgH2O m-2 yr-1), while the difference in AET values between forests and grasslands was not significant after controlling mean annual precipitation (MAP) relating factors. The effects of latitude and mean annual air temperature (MAT) on AET spatial variations differed between forests and grassland, while AET of forests and grasslands both exhibited increasing trends with similar rates along the increasing MAP, aridity index (AI), soil water content (SW), and leaf area index. The comprehensive effects of multiple factors on AET spatial variations differed between forests and grasslands, while MAP both played a dominating role. The effects of other factors were achieved through their close correlations with MAP. Therefore, forests and grasslands under similar climate had comparable AET values. AET responses to MAP were comparable between ecosystem types. Our findings provided a data basis for understanding AET spatial variation over terrestrial ecosystems of China or globally.

Non-linear intensification of monsoon low-pressure systems by the BSISO

Abstract. More than half of the rainfall brought to the Indian subcontinent by the summer monsoon is associated with low-pressure systems (LPSs). Yet their relationship with the boreal summer intraseasonal oscillation (BSISO) – the dominant intraseasonal forcing on the monsoon – is only superficially understood. Using reanalysis data, we explore the relationship between the BSISO and LPS intensity, propagation and precipitation, and associated underlying mechanisms. The BSISO has a large impact on mean monsoon vorticity and rainfall as it moves northward – maximising both in phases 2–3 over southern India and phases 5–6 over northern India – but a much weaker relationship with total column water vapour. We present evidence that LPS genesis also preferentially follows these phases of the BSISO. We identify significant relationships between BSISO phase and LPS precipitation and propagation: for example, during BSISO phase 5, LPSs over northern India produce 51 % heavier rainfall and propagate northwestward 20 % more quickly. Using a combination of moisture flux linearisation and quasi-geostrophic theory, we show that these relationships are driven by changes to the underlying dynamics rather than the moisture content or thermodynamic structure of the monsoon. Using the example of LPSs over northern India during BSISO phase 5, we show that the vertical structure of anomalous vorticity can be split into contributions from the BSISO background circulation and the non-linear response of the LPS to anomalous BSISO circulation. Complementary hypotheses emerge about the source of this non-linear vorticity response: non-linear frictional convergence and secondary barotropic growth. We show that both are important. The BSISO imparts greater meridional shear on the background state, supporting LPS intensification. The BSISO background and non-linear LPS response both contribute significantly to anomalous boundary layer convergence, and we show through vortex budget arguments that the former supports additional LPS intensification in boundary layer, while the latter supports faster westward propagation. This work therefore yields important insights into the scale interactions controlling one of the dominant synoptic systems contributing to rainfall during the monsoon.

Deep neural network-based spatial gap-filling of MODIS ice surface temperatures over the Arctic using satellite and reanalysis data

ABSTRACT Because ice surface temperature (IST) controls snow melt, sea ice growth and air–ocean heat exchange, it is a key parameter in analyses of the Arctic climate system. However, optical satellite-based IST data often have missing values due to the satellite orbit, clouds and polar night. In this study, we estimated IST using a deep neural network (DNN) algorithm based on meteorological, sea ice and geometric variables that are strongly correlated with IST. Moderate-Resolution Imaging Spectroradiometer (MODIS)/Terra IST data were used, and the input data were 2 m air temperature (Ta), 30-year averaged Ta (Ta climatology), total column water vapour (TCWV), solar zenith angle (SZA), local solar noon angle (LSN) and latitude. The data were classified into six cases according to sea ice age (SIA) and Ta to create an efficient DNN model that supplemented the IST data missing from the existing dataset. Model validation based on the MODIS IST data revealed a correlation coefficient of 0.94, root mean square error of 3.54 K and relative root mean square error of 1.35%, showing high accuracy.

Exploring Methods to Estimate Ice Sublimation and Total Water Vapor Flux of Large Lakes in China

AbstractLake ice sublimation plays an important role in lake water budget, while there is huge uncertainty in estimation of lake ice sublimation and its proportion in annual lake total water vapor flux. In the present study, we investigated the annual total water vapor flux and ice sublimation at 15 frozen lakes or lake groups in China using three different methods, namely, the Community Land Model (CLM) simulations (Penman‐Monteith equation and pan observations) that considers (could not reflect) heat storage of lakes; the potential changes of lake total water vapor flux and ice sublimation with climate warming were also predicted. The results illustrate that the annual lake total water vapor flux obtained from Penman‐Monteith equation, pan observation, CLM simulation decreased in sequence, and ice sublimation obtained from pan observation, Penman‐Monteith equation, CLM simulation decreased in sequence. The lake ice sublimation from CLM occupied 7.4%–26.6% of corresponding annual lake total water vapor flux during 1950s–2010s in different regions of China. The ice sublimation and its proportion in lake total water vapor flux from these three methods were all highly correlated, with R2 being 0.86–0.97. The annual lake total water vapor flux will significantly increase in all frozen‐lake regions in China with the climate warming. Climate warming potentially reduce sublimation at most lakes.

Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI

Abstract. Mitigating the impact of atmospheric effects on optical remote sensing data is critical for monitoring intrinsic land processes and developing Analysis Ready Data (ARD). This work develops an approach to this for the NERC NCEO medium resolution ARD Landsat 8 (L8) and Sentinel 2 (S2) products, called Sensor Invariant Atmospheric Correction (SIAC). The contribution of the work is to phrase and solve that problem within a probabilistic (Bayesian) framework for medium resolution multispectral sensors S2/MSI and L8/OLI and to provide per-pixel uncertainty estimates traceable from assumed top-of-atmosphere (TOA) measurement uncertainty, making progress towards an important aspect of CEOS ARD target requirements. A set of observational and a priori constraints are developed in SIAC to constrain an estimate of coarse resolution (500 m) aerosol optical thickness (AOT) and total column water vapour (TCWV), along with associated uncertainty. This is then used to estimate the medium resolution (10–60 m) surface reflectance and uncertainty, given an assumed uncertainty of 5 % in TOA reflectance. The coarse resolution a priori constraints used are the MODIS MCD43 BRDF/Albedo product, giving a constraint on 500 m surface reflectance, and the Copernicus Atmosphere Monitoring Service (CAMS) operational forecasts of AOT and TCWV, providing estimates of atmospheric state at core 40 km spatial resolution, with an associated 500 m resolution spatial correlation model. The mapping in spatial scale between medium resolution observations and the coarser resolution constraints is achieved using a calibrated effective point spread function for MCD43. Efficient approximations (emulators) to the outputs of the 6S atmospheric radiative transfer code are used to estimate the state parameters in the atmospheric correction stage. SIAC is demonstrated for a set of global S2 and L8 images covering AERONET and RadCalNet sites. AOT retrievals show a very high correlation to AERONET estimates (correlation coefficient around 0.86, RMSE of 0.07 for both sensors), although with a small bias in AOT. TCWV is accurately retrieved from both sensors (correlation coefficient over 0.96, RMSE &lt;0.32 g cm−2). Comparisons with in situ surface reflectance measurements from the RadCalNet network show that SIAC provides accurate estimates of surface reflectance across the entire spectrum, with RMSE mismatches with the reference data between 0.01 and 0.02 in units of reflectance for both S2 and L8. For near-simultaneous S2 and L8 acquisitions, there is a very tight relationship (correlation coefficient over 0.95 for all common bands) between surface reflectance from both sensors, with negligible biases. Uncertainty estimates are assessed through discrepancy analysis and are found to provide viable estimates for AOT and TCWV. For surface reflectance, they give conservative estimates of uncertainty, suggesting that a lower estimate of TOA reflectance uncertainty might be appropriate.

Impact of cloud and total column water vapor on annual performance of passive daytime radiative cooler

The surface temperature of a passive daytime radiative cooler (PDRC) can be lower than the ambient temperature without consuming any extra energy. Thus, the technology of the PDRC has attracted the attention of researchers recently. However, most current studies only focused on the short-term performance of the PDRC during sunny and cloudless days. The influence of clouds and variations of atmosphere total column water vapor on the long-term performance of PDRC have not been investigated in previous research. In this study, a radiative cooling model considering the influence of total column water vapor and clouds was developed for PDRC, and TMY weather data files with total column water vapor were generated for estimating the annual performance of PDRC in five selected major climate zones in China. The results showed that the high total column water vapor would deteriorate the performance of the PDRC. Considering the impact of cloud or not, the difference in potential of achieving sub-ambient cooling during daytime annually ranged from 12.68 % to 43.35 % in five cities, i.e., Harbin, Beijing, Shanghai, Kunming, and Guangzhou. The results also indicated that the selective PDRC always achieved a slightly better performance than the broadband one. Finally, the potential of applying PDRC in five selected cities was estimated. The results demonstrated that the PDRC could yield satisfying performance in Beijing, Shanghai, Guangzhou, and Kunming, with the energy-saving potential of 39.53 kWh/m2, 221.40 kWh/m2, 435.82 kWh/m2, 97.26 kWh/m2, respectively.

Evaluation of tropical water vapour from CMIP6 global climate models using the ESA CCI Water Vapour climate data records

Abstract. The tropospheric water vapour data record generated within the ESA Climate Change Initiative Water Vapour project (ESA TCWV-COMBI) is used to evaluate the interannual variability of global climate models (CMIP6 framework under AMIP scenarios) and reanalysis (ECMWF ERA5). The study focuses on the tropical belt, with a separation of oceanic and continental situations. The intercomparison is performed according to the probability density function (PDF) of the total column water vapour (TCWV) defined yearly from the daily scale, as well as its evolution with respect to large-scale overturning circulation. The observational diagnostic relies on the decomposition of the tropical atmosphere into percentile of the PDF and into dynamical regimes defined from the atmospheric vertical velocity. Large variations are observed in the patterns among the data records over tropical land, while oceanic situations show more similarities in both interannual variations and percentile extremes. The signatures of El Niño and La Niña events, driven by sea surface temperatures, are obvious over the oceans. Differences also occur over land for both trends (a strong moistening is observed in the ESA TCWV-COMBI data record, which is absent in CMIP6 models and ERA5) and extreme years. The discrepancies are probably associated with the scene selection applied in the data process. Since the results are sensitive to the scene selection applied in the data process, discrepancies are observed among the datasets. Therefore, the normalization process is employed to analyse the time evolution with respect to the mean state. Other sources of differences, linked to the models and their parametrizations, are highlighted.

ROLE OF THERMODYNAMIC PROCESSES IN PLANT LEAF GAS EXCHANGE SYSTEM FOR ASSIMILATION OF CO2 EMISSIONS FROM THE AMBIENT AIR

When temperature in the leaf gas exchange system changes, the thermodynamic parameters describing the condition of moist air also change. A temperature change of 1 oC in plant leaf tissues leads to a change in partial water vapour pressure of 144 Pa in the gas exchange cavities. Then a temperature decrease of 1 oC in a plant leaf produces 0.897 g of condensate, from 1 m3 of air in leaf ventilation cavities on the surface. When the temperature of plant leaves in the leaf ventilation system changes, the total water vapor state on the inner surface of the leaves changes, and the water vapor state in the stomatal cavities changes. The thickness of the formed condensate film on the plant leaf canal wall surfaces depends on the canal diameter and temperature change. The paper presents information about the mechanism of water formation and thermodynamic processes in the plant leaf gas exchange system participating in the process of assimilation. The formation and change of the internal surfaces of the stomatal cavities of the water film sheet allow the participation of chemical processes in the assimilation of CO2 emissions from the environment.

Seasonal heavy precipitation sensitivity to moisture corrections in the western Mediterranean across resolutions

The controlling role of atmospheric water vapour for heavy precipitation leading to extreme events has been widely demonstrated, along with the existing gap of adequate moisture observations and the frequent biases present in model simulations concerning this fundamental variable. In this study, we profit from a state-of-the-art dense network of GPS measurements over Europe retrieving a homogenized GPS-derived Zenith Total Delay (GPS-ZTD) data set up to 10 min of temporal resolution, to assess the seasonal sensitivity of convection-related processes and heavy precipitation modelling to atmospheric humidity corrections. For this purpose, we perform nudging experiments with the COSMO-CLM model at two spatial resolutions, 7 km (parameterized convection) and 2.8 km (explicitly resolved convection) covering the autumn period of 2012, when the Special Observation Period (SOP) 1 of the Hydrological Cycle in the Mediterranean Experiment (HyMeX) program took place in the Western Mediterranean, which is our area of interest.The benefits and disadvantages of GPS-ZTD nudging and resulting moisture corrections are disentangled. The impact on high-resolution parameterized versus convection-permitting simulations is compared. A process-understanding methodology and a local-to-regional approach are used. Our results show a beneficial impact on the seasonal scale at both model grid spacings improving the representation of the chain of processes leading to heavy precipitation, contrary to the non-systematic improvement at the event and sub-event scales. The correction of atmospheric moisture entails a reduction of about 10% in the total column water vapour and corrections on single locations up to 10 mm counteracting the model wet bias across scales. The location, structure, and amount of total precipitation are positively affected. Particularly, the combination of high-resolution atmospheric humidity observations and fine convection-permitting simulations shows great potential for correction of the precipitation daily cycle, key for accurate precipitation modelling. The difference in the density of local and upstream observational networks and the lack of information on the vertical stratification of moisture are identified weaknesses, which could be determinants in obtaining more accurate corrections on seasonal to sub-seasonal scales after assimilation strategies.

Analysis of the main source regions of moisture transport events with the new ESA CCI/CM‐SAF total column water vapour climate data record (v2)

AbstractThis study makes use of the new total column water vapour data record (CDR‐2 (v2)), developed by the European Space Agency (ESA) in coordination with the Satellite Application Facility on Climate Monitoring (CM SAF), to analyse the adequacy of the integrated vertical water vapour column (IWV) data provided by the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA5 and ERA‐Interim reanalyses in regions of critical interest for moisture transport mechanisms. This information is critical for the initialization of moisture transport models—both Eulerian and Lagrangian—used to study the main mechanisms and predict the future evolution of moisture transport events. In particular, almost 40,000 atmospheric river (AR) and nocturnal low‐level jet (NLLJ) events identified on a global scale between 2002 and 2017 have been used to study the variability between the cited reanalyses and CDR‐2, in terms of both bias in the observed values of IWV during each particular event and daily temporal correlation fields. Although some notable discrepancies are reported in the main tropical rainforest regions, it is observed that, in regions of high interest for both ARs and NLLJs, the degree of agreement between the reanalyses and CDR‐2 is high. The bias observed in the regions of interest is generally low, and the temporal correlation in the IWV fields is above 0.8 in most areas. ERA5 appears to show slightly better performance than ERA‐Interim when resolving the moisture column, and both show greater similarity to CDR‐2 in the midlatitudes compared with tropical regions. The probability density functions constructed on an event‐to‐event basis reinforce these ideas. We conclude that the evaluations presented here using CDR‐2 serve to strengthen avaliable evidence that the ECMWF reanalyses can safely be used in the initializations of Lagrangian dispersion models and Eulerian moisture tracer simulations—commonly used for the analysis of main advection mechanisms—in the vast majority of regions critical to the study of ARs and LLJs. They can also safely be used for the detection of moisture source–sink regions in the study of the global hydrological cycle in these regions.

Comparative analysis of two flow decomposition approaches and their applications in diagnosing water vapor in extreme precipitation events over Xinjiang province, China

Water vapor flux divergence under the terrain-following coordinate system is decomposed into slow and fast manifolds with the adoption of the Barnes filter method and the synoptic approach based on the Helmholtz theorem in this study. The performances and applications of both methods are evaluated in precipitating events over the Xinjiang province in China when traditional analysis fails to capture the evolutions of extreme precipitations caused by multi-scale systems and complicated terrains. A blizzard with a short duration under weak moisture environments and a persistent torrential rainfall event with abundant water vapor in the summer are selected as test examples for comparative analysis of the relationships between water vapor divergence and precipitation characteristics with the adoptions of both traditional synoptic diagnosis and two decomposition methods. It is found that divergent areas of the total water vapor flux divergence at the middle and high levels are blocked by false convergence at the low levels as the precipitation weakens in both cases, which leads to the failure of traditional synoptic diagnoses. Meanwhile, the two decomposition methods can provide superior indicators for extreme precipitation, especially before rainfall decreases or terminates. The synoptic approach performs better than the Barnes filter method as the latter sharply decreases in accuracy as the complexity of the flow fields and moisture conditions increase. Remedies for relieving this problem are proposed before being used for further applications.

Analysis of global trends of total column water vapour from multiple years of OMI observations

Abstract. In this study, we investigate trends in total column water vapour (TCWV) retrieved from measurements of the Ozone Monitoring Instrument (OMI) for the time range between January 2005 to December 2020. The trend analysis reveals, on global average, an annual increase in the TCWV amount of approximately +0.054 kg m−2 yr−1 or +0.21 % yr−1. After the application of a Z test (to the significance level of 5 %) and a false discovery rate (FDR) test to the results of the trend analysis, mainly positive trends remain, in particular over the northern subtropics in the eastern Pacific. Combining the relative TCWV trends with trends in air temperature, we also analyse trends in relative humidity (RH) on the local scale. This analysis reveals that the assumption of temporally invariant RH is not always fulfilled, as we obtain increasing and decreasing RH trends over large areas of the ocean and land surface and also observe that these trends are not limited to arid and humid regions, respectively. For instance, we find decreasing RH trends over the (humid) tropical Pacific Ocean in the region of the Intertropical Convergence Zone. Interestingly, these decreasing RH trends in the tropical Pacific Ocean coincide well with decreasing trends in precipitation. Moreover, by combining the trends of TCWV, surface temperature, and precipitation, we derive trends for the global water vapour turnover time (TUT) of approximately +0.02 d yr−1. Also, we obtain a TUT rate of change of around 8.4 % K−1, which is 2 to 3 times higher than the values obtained in previous studies.

Probing the Atmospheric Precipitable Water Vapor with SOFIA, Part III. Atlas of Seasonal Median PWV Maps from ERA5, Implications for FIFI-LS and in situ Comparison Between the ERA5 and MERRA-2 Atmospheric Re-analyses

The Stratospheric Observatory for Infrared Astronomy (SOFIA) is an airborne observatory for far-infrared astronomy stationed at the Armstrong Flight Research Center (AFRC) in Palmdale, CA, USA. Although SOFIA flies at altitudes of ∼41,000 ft, any far-infrared observations from within the Earth’s atmosphere are nevertheless hampered by water vapor absorbing the astronomical signal. The primary atmospheric parameter governing absorption in the far-infrared is the total upward precipitable water vapor, PWV. In this paper we present global PWV maps derived from re-analyses from the European Centre for Medium-Range Weather Forecasts, ECMWF, with a geographical resolution of 0.°5, for flight altitudes ranging from 37,000 ft to 45,000 ft and each meteorological season. These maps were validated with FIFI-LS PWV measurements on board SOFIA and allow an investigation of the global morphology and seasonal dependence of the total upward PWV in the stratosphere. We additionally investigate the observing conditions, in terms of PWV, at various locations, especially around SOFIA’s home base, Palmdale, but also around sites in the southern hemisphere like Tahiti, Santiago de Chile (Chile), Buenos Aires (Argentina), and Christchurch (New Zealand). From the southern sites investigated Christchurch provides the best conditions in terms of PWV (and efficiency), Tahiti the worst. Using total power sky measurements with FIFI-LS we also derive a mean emissivity of the telescope (primary, secondary and tertiary mirror) of ϵ Tel = 20.5 ± 1.6% around the astronomically significant [C ii] emission line. We finally compare atmospheric re-analyses from GEOS (MERRA-2) and ECMWF (ERA5) to our FIFI-LS PWV measurements. Both re-analyses correlate linearly with our FIFI-LS PWV measurements from all flight altitudes but with different scaling factors. However, MERRA-2 correlates significantly less well than ERA5 especially for flight altitudes below 41,000 ft.

A Rainfall Forecast Model Based on GNSS Tropospheric Parameters and BP-NN Algorithm

The occurrence of rainfall is the result of a combination of various meteorological factors. Traditional rainfall early warning models solely use Global Navigation Satellite System (GNSS)-derived Zenith Total Delay (ZTD) or Precipitable Water Vapor (PWV) to forecast rainfall, resulting in a low true detected rate. While non-linear rainfall early warning models based on the Back-Propagation Neural Network (BP-NN) algorithm consider the influences of various meteorological factors, the forecasts often exhibit a high false rate. To further improve the prediction of rainfall, a short-term rainfall early warning model based on the GNSS and BP-NN algorithms is proposed in this study. The method uses the traditional rainfall forecasting model and utilizes the BP-NN algorithm to combine various meteorological factors for rainfall early warning. The results of GNSS and BP-NN together improve the precision of rainfall early warning. Observation data from eight GNSS stations, the fifth-generation reanalysis of European Centre for Medium-Range Weather Forecast (ECMWF ERA5), and temperature, pressure, and rainfall data from corresponding meteorological stations in Ningbo, China were utilized to verify the rainfall early warning model proposed in this study. The results show that the proposed model can complement the advantages of the traditional linear and non-linear rainfall early warning methods. The model can maintain a high True Detected Rate (TDR) of rainfall early warning while simultaneously reducing the False Forecasted Rate (FFR). The average TDR of the eight GNSS stations is 100% and the FFR is 20.75%, which are both better than those of existing traditional linear and non-linear rainfall early warning models.

Diagnosing Instantaneous Forcing and Feedbacks of Downwelling Longwave Radiation at the Surface: A Simple Methodology and Its Application to CMIP5 Models

Abstract Climate models predict large increases in downwelling longwave radiation (DLR) at Earth’s surface as atmospheric CO2 concentrations increase. Here we introduce a novel methodology that allows these increases to be decomposed into direct radiative forcing due to enhanced CO2 and feedbacks due to subsequent changes in atmospheric properties. For the first time, we develop explicit analytic expressions for the radiative forcing and feedbacks, which are calculable from time-mean fields of near-surface air temperature, specific humidity, pressure, total column water vapor, and total cloud fraction. Our methodology captures 90%–98% of the variance in changes in clear-sky and all-sky DLR in five CMIP5 models, with a typical error of less than 10%. The longwave feedbacks are decomposed into contributions from changes in temperature, specific humidity, water vapor height scale, and cloud fraction. We show that changes in specific humidity and height scale are closely linked to changes in near-surface air temperature and therefore, in the global average, that 90% of the increase in all-sky DLR may be attributed to a feedback from increasing near-surface air temperature. Mean-state clouds play a major role in changes in DLR by masking the clear-sky longwave and enhancing the temperature feedback via increased blackbody radiation. The impact of changes in cloud cover (the cloud feedback) on the DLR is small (∼2%) in the global average, but significant in particular geographical regions.