scholarly journals An Intercomparison of GPS RO Retrievals with Colocated Analysis and In Situ Observations within Tropical Cyclones

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
Henry R. Winterbottom ◽  
Qingnong Xiao
Keyword(s):  
Tropical Cyclones ◽  
Global Positioning System ◽  
Global Analysis ◽  
Reanalysis Data ◽  
Satellite Mission ◽  
Data Set ◽  
Global Positioning ◽  
In Situ Observations ◽  
Atmospheric State

Observations from four Global Position System (GPS) Radio Occultation (RO) missions: Global Positioning System/Meteorology, CHAallenging Minisatellite Payload, Satellite de Aplicaciones Cientificas-C, and Constellation Observing System for Meteorology, Ionosphere and Climate and Taiwan's FORMOsa SATellite Mission #3 (COSMIC/FORMOSAT-3) are collected within a 600 km radius and ±180 minute temporal window of all observed tropical cyclones (TCs) from 1995 to 2006 that were recorded in the global hurricane best-track reanalysis data set (Jarvinen et al. (1984); Davis et al. (1984)). A composite analysis of tropical cyclone radial mean temperature and water vapor profiles is carried out using the GPS RO retrievals which are colocated with global analysis profiles and available in situ radiosonde observations. The differences between the respective observations and analysis profiles are quantified and the preliminary results show that the observations collected within TCs correspond favorably with both the analysis and radiosonde profiles which are colocated. It is concluded that GPS RO observations will contribute significantly to the understanding and modeling of TC structures, especially those related to vertical variability of the atmospheric state within TCs.

scholarly journals From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

2008 ◽  
Vol 12 (6) ◽  
pp. 1323-1337 ◽  
Author(s):  
C. Albergel ◽  
C. Rüdiger ◽  
T. Pellarin ◽  
J.-C. Calvet ◽  
N. Fritz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Surface Soil ◽  
Root Zone ◽  
Synthetic Data ◽  
Surface Soil Moisture ◽  
Data Set ◽  
Water Index ◽  
In Situ Observations

Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

scholarly journals Concurrent Measurement of Global Positioning System and Event-Based Physical Activity Data: A Methodological Framework for Integration

2020 ◽  
pp. 1-14
Author(s):  
Anna M.J. Iveson ◽  
Malcolm H. Granat ◽  
Brian M. Ellis ◽  
Philippa M. Dall
Keyword(s):  
Physical Activity ◽  
Outcome Measures ◽  
Global Positioning System ◽  
Gps Data ◽  
Positioning System ◽  
Activity Data ◽  
Data Set ◽  
Physical Activity Data ◽  
Global Positioning ◽  
Event Based

Objective: Global positioning system (GPS) data can add context to physical activity data and have previously been integrated with epoch-based physical activity data. The current study aimed to develop a framework for integrating GPS data and event-based physical activity data (suitable for assessing patterns of behavior). Methods: A convenience data set of concurrent GPS (AMOD) and physical activity (activPAL) data were collected from 69 adults. The GPS data were (semi)regularly sampled every 5 s. The physical activity data output was presented as walking events, which are continuous periods of walking with a time-stamped start time and duration (to nearest 0.1 s). The GPS outcome measures and the potential correspondence of their timing with walking events were identified and a framework was developed describing data integration for each combination of GPS outcome and walking event correspondence. Results: The GPS outcome measures were categorized as those deriving from a single GPS point (e.g., location) or from the difference between successive GPS points (e.g., distance), and could be categorical, scale, or rate outcomes. Walking events were categorized as having zero (13% of walking events, 3% of walking duration), or one or more (52% of walking events, 75% of walking duration) GPS points occurring during the event. Additionally, some walking events did not have GPS points suitably close to allow calculation of outcome measures (31% of walking events, 22% of walking duration). The framework required different integration approaches for each GPS outcome type, and walking events containing zero or more than one GPS points.

scholarly journals In situ conservation of fruit landraces

2010 ◽  
Vol 46 (Special Issue) ◽  
pp. S57-S59 ◽  
Author(s):  
F. Paprštein ◽  
J. Sedlák ◽  
V. Holubec
Keyword(s):  
Global Positioning System ◽  
Natural Habitat ◽  
In Situ Conservation ◽  
Sour Cherry ◽  
Ex Situ ◽  
The Czech Republic ◽  
Fruit Species ◽  
Global Positioning ◽  
Berry Fruits

<I>In situ </I>conservation is considered as conservation of wild biota in the natural habitat (locality). The authors extend the term to cultivated fruit species naturalised in the landscape, such as occasional spontaneous seedlings, and planted material such as old solitary trees among fields, old groves, avenues (country lanes), wind-breaks, and abandoned remnants of orchards. <I>In situ </I>conservation is also used to mark unique materials during collecting expeditions, before they will be taken as <I>ex situ </I>or proclaimed as permanent <I>in situ</I>. Important landraces found within 12 regions of the Czech Republic were registered, evaluated, and <I>in situ </I>localised by Global Positioning System (GPS). The following accessions were marked for in-situ conservation: apple (401), sweet cherry (263), pear (91), plum (42), sour cherry (27), and berry fruits (18).

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 Deep learning for Chinese NOx emission inversion and the integration of in situ observations: a case study on the COVID-19 pandemic

2021 ◽  
Author(s):  
Tai-Long He ◽  
Dylan Jones ◽  
Kazuyuki Miyazaki ◽  
Kevin Bowman ◽  
Zhe Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reanalysis Data ◽  
Data Sets ◽  
Computationally Efficient ◽  
Transport Models ◽  
The Neural Network ◽  
In Situ Observations ◽  
Typical Variation

&lt;p&gt;The COVID-19 pandemic led to the lockdown of over one-third of Chinese cities in early 2020. Observations have shown significant reductions of atmospheric abundances of NO&lt;sub&gt;2&lt;/sub&gt; over China during this period. This change in atmospheric NO&lt;sub&gt;2&lt;/sub&gt; implies a dramatic change in emission of NO&lt;sub&gt;x&lt;/sub&gt;, which provides a unique opportunity to study the response of the chemistry of the atmospheric to large reductions in anthropogenic emissions. We use a deep learning (DL) model to quantify the change in surface emissions of NO&lt;sub&gt;x&lt;/sub&gt; in China that are associated with the observed changes in atmospheric NO&lt;sub&gt;2&lt;/sub&gt; during the lockdown period. Compared to conventional data assimilation systems, deep neural networks are free of the potential errors associated with parameterized subgrid-scale processes. Furthermore, they are not susceptible to the chemical errors typically found in atmospheric chemical transport models. The neural-network-based approach also offers a more computationally efficient means of inverse modeling of NO&lt;sub&gt;x&lt;/sub&gt; emissions at high spatial resolutions. Our DL model is trained using meteorological predictors and reanalysis data of surface NO&lt;sub&gt;2&lt;/sub&gt; from 2005 to 2017. The evaluation is conducted using in-situ measurements of NO&lt;sub&gt;2&lt;/sub&gt; in 2019 and 2020. The Baidu 'Qianxi' migration data sets are used to evaluate the model's performance in capturing the typical variation in Chinese NOx emissions during the Chinese New Year holidays. The TROPOMI-derived TCR-2 chemical reanalysis is used to evaluate the DL analysis in 2020. We show that the DL-based approach is able to better reproduce the variation in anthropogenic NO&lt;sub&gt;x&lt;/sub&gt; emissions and capture the reduction in Chinese NO&lt;sub&gt;x&lt;/sub&gt; emissions during the period of the COVID-19 pandemic.&lt;/p&gt;

scholarly journals SEA-LEVEL PRESSURE TRENDS IN THE SOUTHERN OCEAN AND ANTARCTICA FROM REANALYSIS AND IN SITU DATA

2021 ◽  
Vol 49 (4) ◽  
pp. 63-85
Author(s):  
P. Yu. Romanov ◽  
N. A. Romanova
Keyword(s):  
Time Series ◽  
Sea Level ◽  
Reanalysis Data ◽  
Environmental Research ◽  
Sea Level Pressure ◽  
Mean Values ◽  
Level Pressure ◽  
High Latitude Region ◽  
In Situ Observations

Trends in the mean sea-level pressure (SLP) in Antarctica in the last four decades (1980– 2020) have been examined using in situ observations and reanalysis data. The analysis involved time series of monthly mean, season-mean and yearly-mean values of the SLP derived from four reanalysis datasets, NCEP/NCAR, ERA5, JRA55, MERRA2, and from surface observations acquired from the Reference Antarctic Data for Environmental Research (READER) dataset. With this data we have evaluated the trends, characterized their seasonal peculiarities and variation across the high-latitude region of the Southern Hemisphere. The results of the analysis confirmed the dominance of decreasing trends in the annual mean SLP in Antarctica. Larger negative trends were found in the Western Antarctica with the most pronounced pressure drop in the South Pacific. The long-term decrease in the annual mean SLP in Antarctica was due to strong negative pressure trends in the austral summer and fall season whereas in winter and in spring the trends turn to mixed and mostly positive. The comparison of multiyear time series of SLP reanalysis data with in situ observations at Antarctic stations revealed a considerable overestimate of negative SLP trends in the NCEP/NCAR dataset. Among the four examined reanalysis datasets, ERA5 provided the best agreement with the station data on the annual mean and monthly mean SLP trend values.

scholarly journals Estimating the Vertical Structure of Weather-Induced Mission Costs for Small UAS

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2770 ◽  
Author(s):  
John J. Bird ◽  
Scott J. Richardson ◽  
Jack W. Langelaan
Keyword(s):  
Flight Test ◽  
Numerical Weather ◽  
Numerical Weather Model ◽  
Weather Model ◽  
In Situ Observations ◽  
Atmospheric State ◽  
Computational Resources ◽  
The Cost ◽  
Aerial Systems

The performance of small uninhabited aerial systems (UAS) is very sensitive to the atmospheric state. Improving awareness of the environment and its impact on mission performance is important to enabling greater autonomy for small UAS. A modeling system is proposed that allows a small UAS to build a model of the atmospheric state using computational resources available onboard the aircraft and relate the atmospheric state to the cost of completing a mission. In this case, mission cost refers to the energy required per distance traveled. The system can use in situ observations made by the aircraft, but can also incorporate observations from other aircraft and sensors. The modeling system is demonstrated in a flight test aboard a small UAS and validated against radiosondes and numerical weather model analyses. The test demonstrates that the modeling system can represent the atmospheric state and identifies times where significant error exists between the state expected by the numerical weather model and that observed. Transformation of the atmospheric state into a mission performance cost identifies cases where the mission performance cost predicted by a numerical weather model differs from that observed by more than 30%.

Improved global distributions of SF6 and mean age of stratospheric air by use of new spectroscopic data

2020 ◽  
Author(s):  
Gabriele P. Stiller ◽  
Jeremy J. Harrison ◽  
Florian J. Haenel ◽  
Norbert Glatthor ◽  
Sylvia Kellmann ◽  
...  
Keyword(s):  
Cross Sections ◽  
Spectroscopic Data ◽  
Chem Phys ◽  
Spectral Signature ◽  
Data Set ◽  
Sensing Applications ◽  
High Bias ◽  
In Situ Observations ◽  
Global Data

&lt;p&gt;The first and only global data set of mean age of stratospheric air (AoA) with dense day and night coverage has been derived from MIPAS observations by analysis of the spectral signature of SF&lt;sub&gt;6&lt;/sub&gt; (Stiller et al., 2008, 2012; Haenel et al., 2015). Since SF&lt;sub&gt;6&lt;/sub&gt; is a tracer with no sinks in the troposphere and stratosphere and an almost linearly increasing atmospheric abundance, it is often used to derive information on stratospheric transport and mixing due to the Brewer Dobson Circulation, quantified usually as mean age of stratospheric air (AoA). The global data sets of AoA derived so far from MIPAS observations, on basis of spectroscopically measured absorption cross sections by Varanasi et al. (1994), had a high bias in the middle to upper stratosphere compared to balloon-borne in situ observations from the 1990s. By applying a new spectroscopic data set measured in the laboratory recently (J.J. Harrison, to be published), we show that part of the high bias in AoA can be removed, and the residuals between measured and modelled atmospheric spectra can be decreased significantly. In this presentation we discuss the new SF&lt;sub&gt;6&lt;/sub&gt; and AoA distributions, variablilities and trends, and compare to previous versions and independent in situ observations.&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Haenel, F. J., Stiller, G. P., von Clarmann, T., Funke, B., Eckert, E., Glatthor, N., Grabowski, U., Kellmann, S., Kiefer, M., Linden, A., and Reddmann, T.: Reassessment of MIPAS age of air trends and variability, Atmos. Chem. Phys., 15, 13161-13176, https://doi.org/10.5194/acp-15-13161-2015, 2015.&lt;br&gt;Stiller, G. P., von Clarmann, T., H&amp;#246;pfner, M., Glatthor, N., Grabowski, U., Kellmann, S., Kleinert, A., Linden, A., Milz, M., Reddmann, T., Steck, T., Fischer, H., Funke, B., L&amp;#243;pez-Puertas, M., and Engel, A.: Global distribution of mean age of stratospheric air from MIPAS SF6 measurements, Atmos. Chem. Phys., 8, 677-695, https://doi.org/10.5194/acp-8-677-2008, 2008.&lt;br&gt;Stiller, G. P., von Clarmann, T., Haenel, F., Funke, B., Glatthor, N., Grabowski, U., Kellmann, S., Kiefer, M., Linden, A., Lossow, S., and L&amp;#243;pez-Puertas, M.: Observed temporal evolution of global mean age of stratospheric air for the 2002 to 2010 period, Atmos. Chem. Phys., 12, 3311-3331, https://doi.org/10.5194/acp-12-3311-2012, 2012.&lt;br&gt;Varanasi, P., Li, Z., Nemtchinov, V., and Cherukuri, A.: Spectral Absorption&amp;#8211;Coefficient Data on HCFC-22 and SF6 for Remote&amp;#8211; Sensing Applications, J. Quant. Spectrosc. Radiat. Transfer, 52, 323&amp;#8211;332, 1994.&lt;strong&gt;&lt;br&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Characterization of the Long-term Radiosonde Temperature Biases in the Lower Stratosphere using COSMIC and Metop-A/GRAS Data from 2006 to 2014

2016 ◽  
Author(s):  
Shu-Peng Ho ◽  
Liang Peng ◽  
Holger Vömel
Keyword(s):  
Global Positioning System ◽  
Lower Stratosphere ◽  
Temperature Measurements ◽  
Global Trend ◽  
Global Positioning ◽  
The Mean ◽  
Geographical Locations

Abstract. Radiosonde observations (RAOBs) have provided the only long-term global in situ temperature measurements in the troposphere and lower stratosphere since 1958. In this study, we use consistently reprocessed Global Positioning System (GPS) radio occultation (RO) temperature data derived from COSMIC and Metop-A/GRAS missions from 2006 to 2014 to characterize the inter-seasonal and inter-annual variability of temperature biases in the lower stratosphere for different sensor types. The results show that the RAOB temperature biases for different RAOB sensor types are mainly owing to i) uncorrected solar zenith angle dependent errors, and ii) change of radiation correction. The mean daytime temperature difference (ΔT) for Vaisala RS92 is equal to 0.18 K in Australia, 0.20 K in Germany, 0.10 K in Canada, 0.13 K in England, and 0.33 K in Brazil. The mean daytime ΔT is equal to −0.06 K for Sippican, 0.71 K for VIZ-B2, 0.66 K for AVK-MRZ, and 0.18 K for Shanghai. The daytime trend of anomalies for Vaisala RS92 and RO temperature at 50 hPa is equal to 0.00 K/5 yrs over United States, −0.02 K/5 yrs over Germany, 0.17 K/5 yrs over Australia, 0.23 K/5 yrs over Canada, 0.26 K/5 yrs over England, and 0.12 K/5 yrs over Brazil, respectively. Although there still exist uncertainties for Vaisala RS92 temperature measurements over different geographical locations, the global trend of temperature anomaly between Vaisala RS92 and RO from June 2006 to April 2014 is within +/−0.09 K/5 yrs globally. Comparing with Vaisala RS80, Vaisala RS90 and sondes from other manufacturers, the Vaisala RS92 seems to provide the best RAOB temperature measurements, which can potentially be used to construct long term temperature CDRs. Results from this study also demonstrate the feasibility to use RO data to correct RAOB temperature biases for different sensor types.

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