scholarly journals Climatology of free-tropospheric humidity: extension into the SEVIRI era, evaluation and exemplary analysis

2014 ◽  
Vol 14 (20) ◽  
pp. 11129-11148 ◽  
Author(s):  
M. Schröder ◽  
R. Roca ◽  
L. Picon ◽  
A. Kniffka ◽  
H. Brogniez
Keyword(s):  
Standard Deviation ◽  
Relative Humidity ◽  
Water Cycle ◽  
Linear Trend ◽  
Data Set ◽  
Total Uncertainty ◽  
Infrared Imager ◽  
Interannual Standard Deviation ◽  
Uncertainty Estimates ◽  
Data Record

Abstract. A new free-tropospheric humidity (FTH) data record is presented. It is based on observations from the Meteosat Visible and Infrared Imager (MVIRI) onboard Meteosat-2–Meteosat-5, as well as Meteosat-7, and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat-8 and Meteosat-9 at the water absorption band near 6.3 μm. The data set is available under clear-sky and low-level cloud conditions. With the extension to SEVIRI observations, the data record covers the period 1983–2009 with a spatial resolution of 0.625° × 0.625° and a temporal resolution of 3 h. The FTH is the mean relative humidity (RH) in a broad layer in the free troposphere. The relation between the observed brightness temperature (BT) and the FTH is well established. Previous retrievals are refined by taking into account the relative humidity Jacobians in the training process of the statistical retrieval. The temporal coverage is extended into the SEVIRI period, the homogenization of the BT record is improved, and the full archive is reprocessed using updated regression coefficients. The FTH estimated from the Meteosat observations is compared to the FTH computed from the RH profiles of the Analyzed RadioSoundings Archive (ARSA). An average relative bias of −3.2% and a relative root-mean-square difference (RMSD) of 16.8% are observed. This relative RMSD agrees with the outcome of an analysis of the total uncertainty of the FTH product. The decadal stability of the FTH data record is 0.5 ± 0.45% per decade. As exemplary applications, the interannual standard deviation, the differences on decadal scales, and the linear trend in the FTH data record and in the frequency of occurrence of FTH < 10% (FTHp10) are analyzed per season. Interannual standard deviation maxima and maxima in absolute decadal differences are featured in gradient areas between dry and wet regions, as well as in areas where FTH reaches minima and FTHp10 reaches maxima. An analysis of the FTH linear trends and of the associated uncertainty estimates is achieved to identify possible problems with the data record. Positive trends in FTHp10 are featured in gradient areas between wet and dry regions, in regions where the FTH is minimum, in regions where FTHp10 is maximum, and in regions where differences between FTHp10 averaged over the 2000s and 1990s are negative. However, these positive trends in FTHp10 are associated with maximum standard deviation and are thus hardly significant. This analysis and intercomparisons with other humidity data records are part of the Global Energy and Water Cycle Experiment (GEWEX) Water Vapor Assessment (G-VAP).

scholarly journals Climatology of free tropospheric humidity: extension into the SEVIRI era, evaluation and exemplary analysis

2014 ◽  
Vol 14 (7) ◽  
pp. 9603-9646
Author(s):  
M. Schröder ◽  
R. Roca ◽  
L. Picon ◽  
A. Kniffka ◽  
H. Brogniez
Keyword(s):  
Standard Deviation ◽  
Relative Humidity ◽  
Lapse Rate ◽  
Specific Humidity ◽  
Strong Line ◽  
Digital Object Identifier ◽  
Total Uncertainty ◽  
Infrared Imager ◽  
Uncertainty Estimates ◽  
Data Record

Abstract. A new free tropospheric humidity (FTH) data record is presented. It is based on observations of Meteosat-2–5 and Meteosat-7 Meteosat Visible and Infrared Imager (MVIRI) and Meteosat-8 and -9 Spinning Enhanced Visible and Infrared Imager (SEVIRI) at the water absorption band at 6.3 μm. With the extension to SEVIRI observations the data record now covers the period 1983–2009 with a spatial and temporal resolution of 0.625° and 3 h, respectively. The data record is referenced under digital object identifier (doi): 10.5676/EUM_SAF_CM/FTH_METEOSAT/V001 and is freely available from http://www.cmsaf.eu/wui . The relation between the observed brightness temperature (BT) and FTH is well established: the observed BT is proportional to the logarithm of the mean relative humidity (RH). Under the given assumptions, constant lapse rate and random strong line theory, it means that the observed BT is mainly a function of RH alone and not of temperature and specific humidity separately. Here, existing retrievals have been refined mainly through the consideration of relative humidity Jacobians in the training process of the statistical retrieval. The temporal coverage has been extended into the SEVIRI era, the homogenisation of the BT record has been improved and the full archive has been reprocessed using updated regression coefficients. The FTH product is compared against FTH computed on the basis of the Analysed RadioSoundings Archive (ARSA) observations. An average relative bias and root mean square difference (RMSD) of −3.2 and 16.8%, respectively, are observed. The RMSD confirms the expectation from an analysis of the total uncertainty of the FTH product. The decadal stability is 0.5 ± 0.45% per decade. As exemplary applications the inter-annual standard deviation, differences on decadal scales and the linear trend in the FTH data record and the frequency of occurrence of FTH <10% (FTHp10) are analysed per season. Maxima in inter-annual standard deviations as well as maxima in absolute differences occur in gradient areas between dry and wet regions and areas with minima in FTH and maxima in FTHp10. An analysis of the linear trends and associated uncertainty estimates has been attempted to identify possible problems with the data record. Positive trends in FTHp10 coincide with gradient areas and regions of minimum FTH, maximum FTHp10 as well as with negative differences between decadal FTHp10 averages of the 1990s and 2000s. However, they are accompanied by maximum standard deviation and are therefore hardly significant which is also valid for FTH trend estimates. These activities plus inter-comparisons to other humidity data records are part of the Global Energy and Water Exchanges Project (GEWEX) water vapor assessment (G-VAP) and will be extended to other FTH data records in the near future.

scholarly journals Calculating uncertainty for the RICE ice core continuous flow analysis water isotope record

2018 ◽  
Vol 11 (8) ◽  
pp. 4725-4736 ◽  
Author(s):  
Elizabeth D. Keller ◽  
W. Troy Baisden ◽  
Nancy A. N. Bertler ◽  
B. Daniel Emanuelsson ◽  
Silvia Canessa ◽  
...  
Keyword(s):  
High Resolution ◽  
Continuous Flow ◽  
Ice Core ◽  
Flow Analysis ◽  
Calibration Error ◽  
Data Set ◽  
Total Uncertainty ◽  
Continuous Flow Analysis ◽  
Uncertainty Estimates ◽  
Water Isotope

Abstract. We describe a systematic approach to the calibration and uncertainty estimation of a high-resolution continuous flow analysis (CFA) water isotope (δ2H, δ18O) record from the Roosevelt Island Climate Evolution (RICE) Antarctic ice core. Our method establishes robust uncertainty estimates for CFA δ2H and δ18O measurements, comparable to those reported for discrete sample δ2H and δ18O analysis. Data were calibrated using a time-weighted two-point linear calibration with two standards measured both before and after continuously melting 3 or 4 m of ice core. The error at each data point was calculated as the quadrature sum of three factors: Allan variance error, scatter over our averaging interval (error of the variance) and calibration error (error of the mean). Final mean total uncertainty for the entire record is δ2H=0.74 ‰ and δ18O=0.21 ‰. Uncertainties vary through the data set and were exacerbated by a range of factors, which typically could not be isolated due to the requirements of the multi-instrument CFA campaign. These factors likely occurred in combination and included ice quality, ice breaks, upstream equipment failure, contamination with drill fluid and leaks or valve degradation. We demonstrate that our methodology for documenting uncertainty was effective across periods of uneven system performance and delivered a significant achievement in the precision of high-resolution CFA water isotope measurements.

scholarly journals The MSG-SEVIRI-based cloud property data record CLAAS-2

2017 ◽  
Vol 9 (2) ◽  
pp. 415-434 ◽  
Author(s):  
Nikos Benas ◽  
Stephan Finkensieper ◽  
Martin Stengel ◽  
Gerd-Jan van Zadelhoff ◽  
Timo Hanschmann ◽  
...  
Keyword(s):  
Climate Models ◽  
Water Cycle ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Time Frame ◽  
Cloud Property ◽  
Extensive Evaluation ◽  
Infrared Imager ◽  
Property Data ◽  
Data Record

Abstract. Clouds play a central role in the Earth's atmosphere, and satellite observations are crucial for monitoring clouds and understanding their impact on the energy budget and water cycle. Within the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF), a new cloud property data record was derived from geostationary Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) measurements for the time frame 2004–2015. The resulting CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) data record is publicly available via the CM SAF website (https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002). In this paper we present an extensive evaluation of the CLAAS-2 cloud products, which include cloud fractional coverage, thermodynamic phase, cloud top properties, liquid/ice cloud water path and corresponding optical thickness and particle effective radius. Data validation and comparisons were performed on both level 2 (native SEVIRI grid and repeat cycle) and level 3 (daily and monthly averages and histograms) with reference datasets derived from lidar, microwave and passive imager measurements. The evaluation results show very good overall agreement with matching spatial distributions and temporal variability and small biases attributed mainly to differences in sensor characteristics, retrieval approaches, spatial and temporal samplings and viewing geometries. No major discrepancies were found. Underpinned by the good evaluation results, CLAAS-2 demonstrates that it is fit for the envisaged applications, such as process studies of the diurnal cycle of clouds and the evaluation of regional climate models. The data record is planned to be extended and updated in the future.

scholarly journals Assessment of the Relationship between Cerebrospinal Meningitis and Climate Variables in Kaduna State, Nigeria

2021 ◽  
pp. 33-41
Author(s):  
Joyce Imara Nchom ◽  
A. S. Abubakar ◽  
F. O. Arimoro ◽  
B. Y. Mohammed
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Linear Trend ◽  
Pearson Correlation ◽  
Maximum Temperature ◽  
Data Set ◽  
International Research Institute ◽  
Wind Speed Maximum ◽  
Weather Parameters ◽  
The Relationship

This study examines the relationship between Meningitis and weather parameters (air temperature, maximum temperature, relative humidity, and rainfall) in Kaduna state, Nigeria on a weekly basis from 2007–2019. Meningitis data was acquired weekly from Nigeria Centre for Disease Control (NCDC), Bureau of Statistics and weather parameters were sourced from daily satellite data set National Oceanic and Atmospheric Administration (NOAA), International Research Institute for Climate and Society (IRI). The daily data were aggregated weekly to suit the study. The data were analysed using linear trend and Pearson correlation for relationship. The linear trend results revealed a weekly decline in Cerebro Spinal Meningitis (CSM), wind speed, maximum and air temperature and an increase in relative humidity and rainfall. Generally, results reveal that the most important explanatory weather variables influencing CSM amongst the five (5) are the weekly maximum temperature and air temperature with a positive correlation of 0.768 and 0.773. This study recommends that keen interest be placed on temperature as they play an essential role in the transmission of this disease and most times aggravate the patients' condition.

scholarly journals A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010

2018 ◽  
Vol 22 (1) ◽  
pp. 241-263 ◽  
Author(s):  
Yu Zhang ◽  
Ming Pan ◽  
Justin Sheffield ◽  
Amanda L. Siemann ◽  
Colby K. Fisher ◽  
...  
Keyword(s):  
Water Budget ◽  
Water Cycle ◽  
Data Sources ◽  
Climate Data ◽  
Data Set ◽  
Data Record ◽  
Terrestrial Water ◽  
In Situ Observations ◽  
Climate Data Record

Abstract. Closing the terrestrial water budget is necessary to provide consistent estimates of budget components for understanding water resources and changes over time. Given the lack of in situ observations of budget components at anything but local scale, merging information from multiple data sources (e.g., in situ observation, satellite remote sensing, land surface model, and reanalysis) through data assimilation techniques that optimize the estimation of fluxes is a promising approach. Conditioned on the current limited data availability, a systematic method is developed to optimally combine multiple available data sources for precipitation (P), evapotranspiration (ET), runoff (R), and the total water storage change (TWSC) at 0.5∘ spatial resolution globally and to obtain water budget closure (i.e., to enforce P-ET-R-TWSC= 0) through a constrained Kalman filter (CKF) data assimilation technique under the assumption that the deviation from the ensemble mean of all data sources for the same budget variable is used as a proxy of the uncertainty in individual water budget variables. The resulting long-term (1984–2010), monthly 0.5∘ resolution global terrestrial water cycle Climate Data Record (CDR) data set is developed under the auspices of the National Aeronautics and Space Administration (NASA) Earth System Data Records (ESDRs) program. This data set serves to bridge the gap between sparsely gauged regions and the regions with sufficient in situ observations in investigating the temporal and spatial variability in the terrestrial hydrology at multiple scales. The CDR created in this study is validated against in situ measurements like river discharge from the Global Runoff Data Centre (GRDC) and the United States Geological Survey (USGS), and ET from FLUXNET. The data set is shown to be reliable and can serve the scientific community in understanding historical climate variability in water cycle fluxes and stores, benchmarking the current climate, and validating models.

scholarly journals SARAH-3 - a new satellite-based Cimate Data Record for surface radiation parameters from the CM SAF

2021 ◽  
Author(s):  
Uwe Pfeifroth ◽  
Jaqueline Drücke ◽  
Jörg Trentmann ◽  
Rainer Hollmann
Keyword(s):  
Water Cycle ◽  
Sunshine Duration ◽  
Global Radiation ◽  
Surface Radiation ◽  
Climate Data ◽  
Surface Solar Radiation ◽  
Data Set ◽  
Time Period ◽  
Data Record ◽  
Climate Data Record

&lt;p&gt;The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates and distributes high quality long-term climate data records (CDR) of energy and water cycle parameters, which are freely available.&lt;/p&gt;&lt;p&gt;In fall 2021, a new version of the &amp;#8220;Surface Solar Radiation data set &amp;#8211; Heliosat&amp;#8221; will be released: SARAH-3. As the previous editions, the SARAH-3 climate data record is based on satellite observations from the first and second METEOSAT generations and provides various surface radiation parameters, including global radiation, direct radiation, sunshine duration, photosynthetic active radiation and others. SARAH-3 covers the time period 1983 to 2020 and offers 30-minute instantaneous data as well as daily and monthly means on a regular 0.05&amp;#176; x 0.05&amp;#176; lon/lat grid.&lt;/p&gt;&lt;p&gt;In this presentation, an overview of the SARAH climate data record and their applications will be provided. A focus will be on the SARAH-3 developments and improvements (i.e. improved consideration of snow-covered surfaces). First validation results of the new Climate Data Record using surface reference observations will be presented. Further, SARAH-3 will be used for the analysis of the climate variability in Europe during the last decades.&lt;/p&gt;&lt;p&gt;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&lt;/p&gt;

scholarly journals Odin stratospheric proxy NO<sub>y</sub> measurements and climatology

2008 ◽  
Vol 8 (19) ◽  
pp. 5731-5754 ◽  
Author(s):  
S. Brohede ◽  
C. A. McLinden ◽  
J. Urban ◽  
C. S. Haley ◽  
A. I. Jonsson ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Middle Atmosphere ◽  
Potential Temperature ◽  
Data Set ◽  
Infrared Imager ◽  
Accuracy And Precision ◽  
Spatial Coverage ◽  
Data Record ◽  
Temporal And Spatial ◽  
Chemistry Experiment

Abstract. Five years of OSIRIS (Optical Spectrograph and InfraRed Imager System) NO2 and SMR (Sub-millimetre and Millimetre Radiometer) HNO3 observations from the Odin satellite, combined with data from a photochemical box model, have been used to construct a stratospheric proxy NOy data set including the gases: NO, NO2, HNO3, 2×N2O5 and ClONO2. This Odin NOy climatology is based on all daytime measurements and contains monthly mean and standard deviation, expressed as mixing ratio or number density, as function of latitude or equivalent latitude (5° bins) on 17 vertical layers (altitude, pressure or potential temperature) between 14 and 46 km. Comparisons with coincident NOy profiles from the Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS) instrument were used to evaluate several methods to combine Odin observations with model data. This comparison indicates that the most appropriate merging technique uses OSIRIS measurements of NO2, scaled with model NO/NO2 ratios, to estimate NO. The sum of 2×N2O5 and ClONO2 is estimated from uncertainty-based weighted averages of scaled observations of SMR HNO3 and OSIRIS NO2. Comparisons with ACE-FTS suggest the precision (random error) and accuracy (systematic error) of Odin NOy profiles are about 15% and 20%, respectively. Further comparisons between Odin and the Canadian Middle Atmosphere Model (CMAM) show agreement to within 20% and 2 ppb throughout most of the stratosphere except in the polar vortices. The combination of good temporal and spatial coverage, a relatively long data record, and good accuracy and precision make this a valuable NOy product for various atmospheric studies and model assessments.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

scholarly journals Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

2020 ◽  
Vol 17 (10) ◽  
pp. 3730 ◽  
Author(s):  
Sina Shaffiee Haghshenas ◽  
Behrouz Pirouz ◽  
Sami Shaffiee Haghshenas ◽  
Behzad Pirouz ◽  
Patrizia Piro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Infectious Disease ◽  
Relative Humidity ◽  
Cost Function ◽  
Selection Process ◽  
Environmental Parameters ◽  
Pso Algorithm ◽  
Data Set ◽  
De Algorithm

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

scholarly journals MAX-DOAS observations of aerosols, formaldehyde and nitrogen dioxide in the Beijing area: comparison of two profile retrieval approaches

2015 ◽  
Vol 8 (2) ◽  
pp. 941-963 ◽  
Author(s):  
T. Vlemmix ◽  
F. Hendrick ◽  
G. Pinardi ◽  
I. De Smedt ◽  
C. Fayt ◽  
...  
Keyword(s):  
Standard Deviation ◽  
A Priori ◽  
Systematic Bias ◽  
Aerosol Extinction ◽  
Data Set ◽  
Near Surface ◽  
Beijing Area ◽  
Least Squares Fit ◽  
Relative Differences ◽  
Good Agreement

Abstract. A 4-year data set of MAX-DOAS observations in the Beijing area (2008–2012) is analysed with a focus on NO2, HCHO and aerosols. Two very different retrieval methods are applied. Method A describes the tropospheric profile with 13 layers and makes use of the optimal estimation method. Method B uses 2–4 parameters to describe the tropospheric profile and an inversion based on a least-squares fit. For each constituent (NO2, HCHO and aerosols) the retrieval outcomes are compared in terms of tropospheric column densities, surface concentrations and "characteristic profile heights" (i.e. the height below which 75% of the vertically integrated tropospheric column density resides). We find best agreement between the two methods for tropospheric NO2 column densities, with a standard deviation of relative differences below 10%, a correlation of 0.99 and a linear regression with a slope of 1.03. For tropospheric HCHO column densities we find a similar slope, but also a systematic bias of almost 10% which is likely related to differences in profile height. Aerosol optical depths (AODs) retrieved with method B are 20% high compared to method A. They are more in agreement with AERONET measurements, which are on average only 5% lower, however with considerable relative differences (standard deviation ~ 25%). With respect to near-surface volume mixing ratios and aerosol extinction we find considerably larger relative differences: 10 ± 30, −23 ± 28 and −8 ± 33% for aerosols, HCHO and NO2 respectively. The frequency distributions of these near-surface concentrations show however a quite good agreement, and this indicates that near-surface concentrations derived from MAX-DOAS are certainly useful in a climatological sense. A major difference between the two methods is the dynamic range of retrieved characteristic profile heights which is larger for method B than for method A. This effect is most pronounced for HCHO, where retrieved profile shapes with method A are very close to the a priori, and moderate for NO2 and aerosol extinction which on average show quite good agreement for characteristic profile heights below 1.5 km. One of the main advantages of method A is the stability, even under suboptimal conditions (e.g. in the presence of clouds). Method B is generally more unstable and this explains probably a substantial part of the quite large relative differences between the two methods. However, despite a relatively low precision for individual profile retrievals it appears as if seasonally averaged profile heights retrieved with method B are less biased towards a priori assumptions than those retrieved with method A. This gives confidence in the result obtained with method B, namely that aerosol extinction profiles tend on average to be higher than NO2 profiles in spring and summer, whereas they seem on average to be of the same height in winter, a result which is especially relevant in relation to the validation of satellite retrievals.

Sign in / Sign up

Export Citation Format

Share Document