scholarly journals Satellite data assimilation to improve forecasts of volcanic ash concentrations

2016 ◽  
Author(s):  
Guangliang Fu ◽  
Hai-Xiang Lin ◽  
Arnold Heemink ◽  
Arjo Segers ◽  
Fred Prata ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Satellite Data ◽  
Volcanic Ash ◽  
Three Dimensional ◽  
Forecast Accuracy ◽  
Improve Model ◽  
Abstract Data ◽  
Assimilation Scheme

Abstract. Data assimilation is a powerful tool that requires available observations to improve model forecast accuracy. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations into the assimilation scheme. However, these satellite-retrieved data are often two-dimensional (2D), and cannot be easily combined with a three-dimensional (3D) volcanic ash model to continuously improve the volcanic ash state in a data assimilation system. By integrating available data including ash mass loadings, cloud top heights and thickness information, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2D volcanic ash mass loadings to 3D concentrations at the top layer of the ash cloud. Ensemble-based data assimilation is used to continuously assimilate the extracted measurements of ash concentrations. The results show that satellite data assimilation can force the volcanic ash state to match the satellite observations, and that it improves the forecast of the ash state. Comparison with highly accurate aircraft in-situ measurements shows that the effective duration of the improved volcanic ash forecasts is about a half day. It is shown that an effective half-day ash forecast significantly improves the quality of the advice given to aviation over continental Europe.

scholarly journals Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption

2017 ◽  
Vol 17 (2) ◽  
pp. 1187-1205 ◽  
Author(s):  
Guangliang Fu ◽  
Fred Prata ◽  
Hai Xiang Lin ◽  
Arnold Heemink ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Volcanic Ash ◽  
Three Dimensional ◽  
Forecast Accuracy ◽  
Improve Model ◽  
Powerful Approach ◽  
Using Data ◽  
D Model

Abstract. Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and the ash plume is usually vertically located in a narrow band, directly assimilating the 2-D ash mass loadings in a three-dimensional (3-D) volcanic ash model (with an integral observational operator) can usually introduce large artificial/spurious vertical correlations.In this study, we look at an approach to avoid the artificial vertical correlations by not involving the integral operator. By integrating available data of ash mass loadings and cloud top heights, as well as data-based assumptions on thickness, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The 3-D SOO makes the analysis step of assimilation comparable in the 3-D model space.Ensemble-based DA is used to assimilate the extracted measurements of ash concentrations. The results show that satellite DA with SOO can improve the estimate of volcanic ash state and the forecast. Comparison with both satellite-retrieved data and aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts for the distal part of the Eyjafjallajökull volcano is about 6 h.

scholarly journals Application of the Multigrid Data Assimilation Scheme to the China Seas’ Temperature Forecast

2008 ◽  
Vol 25 (11) ◽  
pp. 2106-2116 ◽  
Author(s):  
Wei Li ◽  
Yuanfu Xie ◽  
Zhongjie He ◽  
Guijun Han ◽  
Kexiu Liu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Forecast Accuracy ◽  
China Seas ◽  
Temperature Forecast ◽  
Traditional Scheme ◽  
Data Assimilation Scheme ◽  
Mean Square Errors ◽  
Assimilation Scheme ◽  
The China Seas

Abstract Correlation scales have been used in the traditional scheme of three-dimensional variational data assimilation (3DVAR) to estimate the background (or first guess) error covariance matrix (the 𝗕 matrix in brief) for the numerical forecast and reanalysis of ocean for decades. However, it is challenging to implement this scheme. On the one hand, determining the correlation scales accurately can be difficult. On the other hand, the positive definite of the 𝗕 matrix cannot be guaranteed unless the correlation scales are sufficiently small. Xie et al. indicated that a traditional 3DVAR only corrects certain wavelength errors, and its accuracy depends on the accuracy of the 𝗕 matrix. Generally speaking, the shortwave error cannot be sufficiently corrected until the longwave error is corrected. An inaccurate 𝗕 matrix may mistake longwave errors as shortwave ones, resulting in erroneous analyses. A new 3DVAR data assimilation scheme, called a multigrid data assimilation scheme, is proposed in this paper for quickly minimizing longwave and shortwave errors successively. By assimilating the sea surface temperature and temperature profile observations into a numerical model of the China Seas, this scheme is applied to a retroactive real-time forecast experiment and favorable results are obtained. Compared to the traditional scheme of 3DVAR, this new scheme has higher forecast accuracy and lower root-mean-square errors. Note that the new scheme demonstrates greatly improved numerical efficiency in the analysis procedure.

scholarly journals Synergy between satellite observations and model simulations during extreme events

2018 ◽  
Author(s):  
Anne Wiese ◽  
Joanna Staneva ◽  
Johannes Schultz-Stellenfleth ◽  
Arno Behrens ◽  
Luciana Fenoglio-Marc ◽  
...  
Keyword(s):  
Satellite Data ◽  
Extreme Events ◽  
Wave Model ◽  
Coastal Areas ◽  
In Situ Measurements ◽  
Model Simulations ◽  
Significant Wave ◽  
Operational Analysis

Abstract. In this study, the quality of wind and wave data provided by the new Sentinel-3A satellite is evaluated. We focus on coastal areas, where altimeter data are of lower quality than those for the open ocean. The satellite data of Sentinel-3A, Jason-2 and CryoSat-2 are assessed in a comparison with in situ measurements and spectral wave model (WAM) simulations. The sensitivity of the wave model to wind forcing is evaluated using data with different temporal and spatial resolution, such as ERA-Interim and ERA5 reanalyses, ECMWF operational analysis and short-range forecasts, German Weather Service (DWD) forecasts and regional atmospheric model simulations -coastDat. Numerical simulations show that both the wave model forced using the ERA5 reanalyses and that forced using the ECMWF operational analysis/forecast demonstrate the best capability over the whole study period, as well as during extreme events. To further estimate the variance of the significant wave height of ensemble members for different wind forcings, especially during extreme events, an empirical orthogonal function (EOF) analysis is performed. Intercomparisons between remote sensing and in situ observations demonstrate that the overall quality of the former is good over the North Sea and Baltic Sea throughout the study period, although the significant wave heights estimated based on satellite data tend to be greater than the in situ measurements by 7 cm to 26 cm. The quality of all satellite data near the coastal area decreases; however, within 10 km off the coast, Sentinel-3A performs better than the other two satellites. Analyses in which data from satellite tracks are separated in terms of onshore and offshore flights have been carried out. No substantial differences are found when comparing the statistics for onshore and offshore flights. Moreover, no substantial differences are found between satellite tracks under various metocean conditions. Furthermore, the satellite data quality does not depend on the wind direction relative to the flight direction. Thus, the quality of the data obtained by the new Sentinel-3A satellite over coastal areas is improved compared to that of older satellites.

The Met. Office global three-dimensional variational data assimilation scheme

2000 ◽  
Vol 126 (570) ◽  
pp. 2991-3012 ◽  
Author(s):  
A. C. Lorenc ◽  
S. P. Ballard ◽  
R. S. Bell ◽  
N. B. Ingleby ◽  
P. L. F. Andrews ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

A Parallel Three-dimensional Variational Data Assimilation Scheme

2011 ◽  
Author(s):  
L. D’Amore ◽  
R. Arcucci ◽  
L. Marcellino ◽  
A. Murli ◽  
Theodore E. Simos ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

scholarly journals Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

2021 ◽  
Vol 25 (1) ◽  
pp. 17-40
Author(s):  
Hylke E. Beck ◽  
Ming Pan ◽  
Diego G. Miralles ◽  
Rolf H. Reichle ◽  
Wouter A. Dorigo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Data ◽  
Irrigation Scheduling ◽  
Open Loop ◽  
Spatiotemporal Variability ◽  
Near Surface ◽  
Satellite Retrievals ◽  
Performance Ranking

Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

scholarly journals Assimilation of TES CO into a global CTM: first results

2006 ◽  
Vol 6 (6) ◽  
pp. 11727-11743 ◽  
Author(s):  
N. A. D. Richards ◽  
Q. Li ◽  
K. W. Bowman ◽  
J. R. Worden ◽  
S. S. Kulawik ◽  
...  
Keyword(s):  
Transport Model ◽  
Model Simulation ◽  
Three Dimensional ◽  
Model Bias ◽  
Upper Troposphere ◽  
First Results ◽  
Assimilation Scheme ◽  
Co Observations ◽  
Good Agreement

Abstract. We present results from the first assimilation of carbon monoxide (CO) observations from the Tropospheric Emission Spectrometer (TES) into a global three-dimensional (3-D) chemistry and transport model (CTM). A sequential sub-optimal Kalman filter assimilation scheme (Khattatov et al., 2000) was applied to assimilate TES CO profiles during November 2004 into the GEOS-Chem global 3-D CTM. The assimilation results were compared with MOPITT and MOZAIC observations. The assimilation significantly improves model simulation of CO in the middle to upper troposphere, where the MOPITT versus model bias was reduced by up to two-thirds. Assimilation results show higher levels of CO in the southern tropics, consistent with MOPITT observations. We find good agreement between the TES assimilated model estimates of CO and in situ measurements from the MOZAIC program, which shows a negative bias of up to 10 ppbv in middle and upper tropospheric TES CO. The results demonstrate how assimilation can be used for non-coincident validation of TES CO profile retrievals.

scholarly journals Assimilation of decomposed in-situ directional wave spectra into a numerical wave model on typhoon wave

2013 ◽  
Vol 1 (4) ◽  
pp. 3967-3989
Author(s):  
Y. M. Fan ◽  
H. Günther ◽  
C. C. Kao ◽  
B. C. Lee
Keyword(s):  
Data Assimilation ◽  
Forecast Accuracy ◽  
Wave Model ◽  
Sequential Data ◽  
Wave Spectra ◽  
Typhoon Wave ◽  
Independent Observations ◽  
Directional Wave ◽  
Numerical Wave

Abstract. The purpose of this study was to enhance the accuracy of numerical wave forecasts through data assimilation during typhoon period. A sequential data assimilation scheme was modified to enable its use with partitions of directional wave spectra. The performance of the system was investigated with respect to operational applications specifically for typhoon wave. Two typhoons that occurred in 2006 around Taiwan (Kaemi and Shanshan) were used for this case study. The proposed data assimilation method increased the forecast accuracy in terms of wave parameters, such as wave height and period. After assimilation, the shapes of directional spectra were much closer to those reported from independent observations.

scholarly journals Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco

2018 ◽  
Author(s):  
Mohamed Wassim Baba ◽  
Simon Gascoin ◽  
Lahoucine Hanich
Keyword(s):  
Data Assimilation ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Snow Cover Area ◽  
High Atlas ◽  
Data Assimilation Scheme ◽  
Snow Cover Extent ◽  
Assimilation Scheme ◽  
Sentinel 2

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements in MERRA-2 reanalyses and the full revisit capacity of Sentinel-2.

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