scholarly journals Mapping of Snow Depth by Blending Satellite and In-Situ Data Using Two-Dimensional Optimal Interpolation—Application to AMSR2

2019 ◽  
Vol 11 (24) ◽  
pp. 3049 ◽  
Author(s):  
Cezar Kongoli ◽  
Jeffrey Key ◽  
Thomas M. Smith
Keyword(s):  
North America ◽  
Snow Depth ◽  
Weather Prediction ◽  
Weighted Average ◽  
Optimal Interpolation ◽  
Horizontal Distance ◽  
Two Dimensional ◽  
In Situ Data ◽  
The Difference

The development of a snow depth product over North America is investigated by applying two-dimensional optimal interpolation to passive microwave satellite-derived and in-situ measured snow depth. At each snow-covered satellite footprint, the technique computes a snow depth increment as the weighted average of data increments, and updates the satellite-derived snow depth accordingly. Data increments are computed as the difference between the in-situ-measured and satellite snow depth at station locations surrounding the satellite footprint. Calculation of optimal weights is based on spatial lag autocorrelation of snow depth increments, modelled as functions of horizontal distance and elevation difference between pairs of observations. The technique is applied to Advanced Microwave Scanning Radiometer 2 (AMSR2) snow depth and in-situ snow depth obtained from the Global Historical Climatology Network. The results over North America during January–February 2017 indicate that the technique greatly enhances the performance of the satellite estimates, especially over mountain terrain, albeit with an accuracy inferior to that over low elevation areas. Moreover, the technique generates more accurate output compared to that from NOAA’s Global Forecast System, with implications for improving the utilization of satellite data in snow assessments and numerical weather prediction.

scholarly journals Freeboard, snow depth and sea-ice roughness in East Antarctica from in situ and multiple satellite data

2011 ◽  
Vol 52 (57) ◽  
pp. 242-248 ◽  
Author(s):  
Thorsten Markus ◽  
Robert Massom ◽  
Anthony Worby ◽  
Victoria Lytle ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Correlation Coefficient ◽  
Satellite Data ◽  
Snow Depth ◽  
Large Scale ◽  
Mean Difference ◽  
Laser Altimeter ◽  
In Situ Data ◽  
Ice Roughness

AbstractIn October 2003 a campaign on board the Australian icebreaker Aurora Australis had the objective to validate standard Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice products. Additionally, the satellite laser altimeter on the Ice, Cloud and land Elevation Satellite (ICESat) was in operation. To capture the large-scale information on the sea-ice conditions necessary for satellite validation, the measurement strategy was to obtain large-scale sea-ice statistics using extensive sea-ice measurements in a Lagrangian approach. A drifting buoy array, spanning initially 50 km × 100 km, was surveyed during the campaign. In situ measurements consisted of 12 transects, 50–500 m, with detailed snow and ice measurements as well as random snow depth sampling of floes within the buoy array using helicopters. In order to increase the amount of coincident in situ and satellite data an approach has been developed to extrapolate measurements in time and in space. Assuming no change in snow depth and freeboard occurred during the period of the campaign on the floes surveyed, we use buoy ice-drift information as well as daily estimates of thin-ice fraction and rough-ice vs smooth-ice fractions from AMSR-E and QuikSCAT, respectively, to estimate kilometer-scale snow depth and freeboard for other days. the results show that ICESat freeboard estimates have a mean difference of 1.8 cm when compared with the in situ data and a correlation coefficient of 0.6. Furthermore, incorporating ICESat roughness information into the AMSR-E snow depth algorithm significantly improves snow depth retrievals. Snow depth retrievals using a combination of AMSR-E and ICESat data agree with in situ data with a mean difference of 2.3 cm and a correlation coefficient of 0.84 with a negligible bias.

scholarly journals Atmospheric OH reactivity in central London: observations, model predictions and estimates of in situ ozone production

2015 ◽  
Vol 15 (21) ◽  
pp. 31247-31286
Author(s):  
L. K. Whalley ◽  
D. Stone ◽  
B. Bandy ◽  
R. Dunmore ◽  
J. F. Hamilton ◽  
...  
Keyword(s):  
Degradation Mechanism ◽  
Oxidation Products ◽  
Chemical Mechanism ◽  
Ozone Production ◽  
Two Dimensional ◽  
Diurnal Profile ◽  
Biogenic Compounds ◽  
Master Chemical Mechanism ◽  
The Difference

Abstract. Near-continuous measurements of OH reactivity in the urban background atmosphere of central London during the summer of 2012 are presented. OH reactivity behaviour is seen to be broadly dependent on airmass origin with the highest reactivity and the most pronounced diurnal profile observed when air had passed over central London to the East, prior to measurement. Averaged over the entire observation period of 26 days, OH reactivity peaked at ~ 27 s−1 in the morning with a minimum of ~ 15 s−1 during the afternoon. A maximum OH reactivity of 116 s−1 was recorded on one day during morning rush hour. A detailed box model using the Master Chemical Mechanism was used to calculate OH reactivity, and was constrained with an extended measurement dataset of volatile organic compounds (VOCs) derived from GC-FID and a two-dimensional GC instrument which included heavier molecular weight (up to C12) aliphatic VOCs, oxygenated VOCs and the biogenic VOCs of α pinene and limonene. Comparison was made between observed OH reactivity and modelled OH reactivity using (i) a standard suite of VOC measurements (C2-C8 hydrocarbons and a small selection of oxygenated VOCs) and (ii) a more comprehensive inventory including species up to C12. Modelled reactivities were lower than those measured (by 33 %) when only the reactivity of the standard VOC suite was considered. The difference between measured and modelled reactivity was improved, to within 15 %, if the reactivity of the higher VOCs (≥ C9) was also considered, with the reactivity of the biogenic compounds of α pinene and limonene and their oxidation products almost entirely responsible for this improvement. Further improvements in the model's ability to reproduce OH reactivity (to within 6 %) could be achieved if the reactivity and degradation mechanism of unassigned two-dimensional GC peaks were estimated. Neglecting the contribution of the higher VOCs (≥ C9) (particularly α pinene and limonene) and model-generated intermediates worsened the agreement between modelled and observed OH concentrations (by 41 %) and the magnitude of in situ ozone production calculated from the production of RO2 was significantly lower (60 %). This work highlights that any future ozone abatement strategies should consider the role that biogenic emissions play alongside anthropogenic emissions in influencing London's air quality.

scholarly journals Small scale spatial variability of bare-ice albedo at Jamtalferner, Austria

2020 ◽  
Author(s):  
Lea Hartl ◽  
Lucia Felbauer ◽  
Gabriele Schwaizer ◽  
Andrea Fischer
Keyword(s):  
Satellite Data ◽  
Full Range ◽  
Temporal Variations ◽  
Small Scale ◽  
Landsat 8 ◽  
Dry Ice ◽  
In Situ Data ◽  
Depth Analysis ◽  
The Difference

Abstract. As Alpine glaciers recede, they are quickly becoming snow free in summer and, accordingly, spatial and temporal variations in ice albedo increasingly affect the melt regime. To accurately model future developments, such as deglaciation patterns, it is important to understand the processes governing broadband and spectral albedo at a local scale. However, little in situ data of ice albedo exits. As a contribution to this knowledge gap, we present spectral reflectance data from 325 to 1075 nm collected along several profile lines in the ablation zone of Jamtalferner, Austria. Measurements were timed to closely coincide with a Sentinel 2 and Landsat 8 overpass and are compared to the respective ground reflectance products. The brightest spectra have a maximum reflectance of up to 0.7 and consist of clean, dry ice. In contrast, reflectance does not exceed 0.2 at dark spectra where liquid water and/or fine grained debris are present. Spectra can roughly be grouped into dry ice, wet ice, and dirt/rocks, although transitions between types are fluid. Neither satellite captures the full range of in situ reflectance values. The difference between ground and satellite data is not uniform across satellite bands, between Landsat and Sentinel, and to some extent between ice surface types (underestimation of reflectance for bright surfaces, overestimation for dark surfaces). We wish to highlight the need for further, systematic measurements of in situ spectral albedo, its variability in time and space, and in- depth analysis of time-synchronous satellite data.

scholarly journals Advances in altimetric snow depth estimates using bi-frequency SARAL/CryoSat-2 Ka/Ku measurements

2021 ◽  
Author(s):  
Florent Garnier ◽  
Sara Fleury ◽  
Gilles Garric ◽  
Jérôme Bouffard ◽  
Michel Tsamados ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ocean Modeling ◽  
The Arctic ◽  
Validation Data ◽  
Band Frequency ◽  
In Situ Data ◽  
Wide Range ◽  
Assimilation System

Abstract. Although snow depth on sea ice is a key parameter for Sea Ice Thickness (SIT), there currently does not exist reliable estimations. In Arctic, nearly all SIT products use a snow depth climatology (the Warren-99 modified climatology, W99m) constructed from in-situ data obtained prior to the first significant impacts of climate change. In Antarctica, the lack of information on snow depth remains a major obstacle in the development of reliable SIT products. In this study, we present the latest version of the Altimetric Snow Depth (ASD) product computed over both hemispheres from the difference of the radar penetration into the snow pack between the CryoSat-2 Ku-band and the SARAL Ka-band frequency radars. The ASD solution is compared against a wide range of snow depth products including model data (Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) or its equivalent in Antarctica the Global Ice-Ocean Modeling and Assimilation System (GIOMAS), the MERCATOR model and NASA's Eulerian Snow On Sea Ice Model (NESOSIM, only in Arctic)), the Advanced Microwave Scanning Radiometer 2 (AMSR-2) passive radiometer data, and the Dual-altimeter Snow Thickness (DuST) Ka-Ku product (only in Arctic). It is validated in the Arctic against in-situ and airborne validation data. These comparisons demonstrate that ASD provide a consistent snow depth solution, with space and time patterns comparable with those of the alternative Ka-Ku DuST product, but with a mean bias of about 6.5 cm. We also demonstrate that ASD is consistent with the validation data. Comparisons with Operation Ice Bridge's (OIB) airborne snow radar in Arctic during the period of 2014–2018 show a correlation of 0.66 and a RMSE of about 6 cm. Furthermore, a first-guess monthly climatology has been constructed in Arctic from the ASD product, which shows a good agreement with OIB during 2009–2012. This climatology is shown to provide a better solution than the W99m climatology when compared with validation data. Finally, we have characterised the SIT uncertainty due to the snow depth from an ensemble of SIT solutions computed for the Arctic by using the different snow depth products previously used in the comparison with the ASD product. During the period of 2013–2019, we found a spatially averaged SIT mean standard deviation of 20 cm. Deviations between SIT estimations due to different snow depths can reach up to 77 cm. Using the ASD data instead of W99m to estimate SIT over this time period leads to a reduction of the average SIT of about 30 cm.

scholarly journals Numerical experiments in calculating snow water equivalent and estimating their impact on the formation of spring floods

2020 ◽  
Vol 163 ◽  
pp. 06003
Author(s):  
Evgenii Churiulin ◽  
Vladimir Kopeykin ◽  
Natalia Frolova ◽  
Inna Krylenko
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Hydrological Models ◽  
In Situ Data ◽  
Use Of Data ◽  
Complex Approach ◽  
Snow Water ◽  
Seasonal Snow Cover

Seasonal snow cover has a significant impact on forming spring floods. Sparse snow course-measuring network does not meet the requirements of modern tasks related to the technologies of numerical weather prediction (NWP) systems and runoff formation models. Moreover, insufficient volume of hydrometeorological data creates a need to improve spring floods forecasting methods by means of available modern hydrometeorological information related to snow cover. To work out an efficient solution to the issue of initial snow data preparation we need a complex approach including the use of data from satellite, atmospheric models, physical-mathematical models of snow cover and insitu information. This approach will provide modern NWP and hydrological models with reliable initial data on snow cover (snow water equivalent – SWE, snow density – SD). The main purpose of our investigation is related to approbation of satellite data and development of snow cover calculation methods for NWP and hydrological models. Numerous SWE and SD experiments have been performed in order to achieve this aim. A regional snow data assimilation system for COSMORu was implemented during the research. Moreover, a new method of hydrological modelling of spring floods based on ECOMAG model with initial information from COSMO-Ru, SnoWE and in-situ data has been proposed and tested.

scholarly journals Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions

2018 ◽  
Vol 10 (11) ◽  
pp. 1772 ◽  
Author(s):  
Estrella Olmedo ◽  
Carolina Gabarró ◽  
Verónica González-Gambau ◽  
Justino Martínez ◽  
Joaquim Ballabrera-Poy ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Remotely Sensed ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
In Situ Data ◽  
Arctic Regions ◽  
The Difference

This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans ( 50 ∘ N– 90 ∘ N). The SMOS SSS maps presented in this work are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models.

Objective Data Analysis Conference

1987 ◽  
Vol 68 (5) ◽  
pp. 481-485 ◽  
Author(s):  
Edward L. Carr
Keyword(s):  
Data Analysis ◽  
Air Force ◽  
Weather Prediction ◽  
Current Status ◽  
Optimal Interpolation ◽  
Objective Data ◽  
Analysis Model ◽  
Group Discussions ◽  
Computational Aspects ◽  
The Difference

The Air Force Global Weather Central (AFGWC) hosted a conference on objective data analysis 2–4 October 1985. This was a continuation in a series of conferences on data analysis. Participants included both operational meteorologists and research meteorologists from various government's numerical weather prediction (NWP) facilities. The conference consisted of each participating facility reviewing their current status and future plans. Individual topics were then presented concerning data analysis, followed by small group discussions on meteorological and computational aspects of objective data analysis. Various conclusions were developed during the conference: 1) Optimal interpolation is the analysis method most commonly used at NWP facilities and is favored for its ability to assimilate data from many different observing platforms. 2) Promising work continues with the insertion of new data into the analysis model. 3) The fundamental difference between most analyses is the difference in the imposed quality control. 4) Further improvements in data assimilation are possible if archival procedures are performed to gather covariance statistics.

Sign in / Sign up

Export Citation Format

Share Document