scholarly journals Regional data assimilation of multi-spectral MOPITT observations of CO over North America

2015 ◽  
Vol 15 (4) ◽  
pp. 5327-5358 ◽  
Author(s):  
Z. Jiang ◽  
D. B. A. Jones ◽  
J. Worden ◽  
H. M. Worden ◽  
D. K. Henze ◽  
...  
Keyword(s):  
High Resolution ◽  
North America ◽  
Data Assimilation ◽  
Boundary Conditions ◽  
North American ◽  
A Priori ◽  
Small Scale ◽  
Lateral Boundary ◽  
Free Troposphere ◽  
Lateral Boundary Conditions

Abstract. Chemical transport models (CTMs) driven with high-resolution meteorological fields can better resolve small-scale processes, such as frontal lifting or deep convection, and thus improve the simulation and emission estimates of tropospheric trace gases. In this work, we explore the use of the GEOS-Chem four-dimensional variational (4-D-Var) data assimilation system with the nested high-resolution version of the model (0.5° × 0.67°) to quantify North American CO emissions during the period of June 2004 – May 2005. With optimized lateral boundary conditions, regional inversion analyses can reduce the sensitivity of the CO source estimates to errors in long-range transport and in the distributions of the hydroxyl radical (OH), the main sink for CO. To further limit the potential impact of discrepancies in chemical aging of air in the free troposphere, associated with errors in OH, we use surface level multispectral MOPITT CO retrievals, which have greater sensitivity to CO near the surface and reduced sensitivity in the free troposphere, compared to previous versions of the retrievals. We estimate that the annual total anthropogenic CO emission from the contiguous US 48 states was 97 Tg CO, a 14% increase from the 85 Tg CO in the a priori. This increase is mainly due to enhanced emissions around the Great Lakes region and along the west coast, relative to the a priori. Sensitivity analyses using different OH fields and lateral boundary conditions suggest a possible error, associated with local North America OH distribution, in these emission estimates of 20% during summer 2004, when the CO lifetime is short. This 20% OH-related error is 50% smaller than the OH-related error previously estimated for North American CO emissions using a global inversion analysis. We believe that reducing this OH-related error further will require integrating additional observations to provide a strong constraint on the CO distribution across the domain. Despite these limitations, our results show the potential advantages of combining high-resolution regional inversion analyses with global analyses to better quantify regional CO source estimates.

scholarly journals Regional data assimilation of multi-spectral MOPITT observations of CO over North America

2015 ◽  
Vol 15 (12) ◽  
pp. 6801-6814 ◽  
Author(s):  
Z. Jiang ◽  
D. B. A. Jones ◽  
J. Worden ◽  
H. M. Worden ◽  
D. K. Henze ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Boundary Conditions ◽  
North American ◽  
A Priori ◽  
Sensitivity Analyses ◽  
Small Scale ◽  
Lateral Boundary ◽  
Free Troposphere ◽  
Lateral Boundary Conditions

Abstract. Chemical transport models (CTMs) driven with high-resolution meteorological fields can better resolve small-scale processes, such as frontal lifting or deep convection, and thus improve the simulation and emission estimates of tropospheric trace gases. In this work, we explore the use of the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system with the nested high-resolution version of the model (0.5° × 0.67°) to quantify North American CO emissions during the period of June 2004–May 2005. With optimized lateral boundary conditions, regional inversion analyses can reduce the sensitivity of the CO source estimates to errors in long-range transport and in the distributions of the hydroxyl radical (OH), the main sink for CO. To further limit the potential impact of discrepancies in chemical aging of air in the free troposphere, associated with errors in OH, we use surface-level multispectral MOPITT (Measurement of Pollution in The Troposphere) CO retrievals, which have greater sensitivity to CO near the surface and reduced sensitivity in the free troposphere, compared to previous versions of the retrievals. We estimate that the annual total anthropogenic CO emission from the contiguous US 48 states was 97 Tg CO, a 14 % increase from the 85 Tg CO in the a priori. This increase is mainly due to enhanced emissions around the Great Lakes region and along the west coast, relative to the a priori. Sensitivity analyses using different OH fields and lateral boundary conditions suggest a possible error, associated with local North American OH distribution, in these emission estimates of 20 % during summer 2004, when the CO lifetime is short. This 20 % OH-related error is 50 % smaller than the OH-related error previously estimated for North American CO emissions using a global inversion analysis. We believe that reducing this OH-related error further will require integrating additional observations to provide a strong constraint on the CO distribution across the domain. Despite these limitations, our results show the potential advantages of combining high-resolution regional inversion analyses with global analyses to better quantify regional CO source estimates.

scholarly journals Advanced error diagnostics of the CMAQ and Chimere modelling systems within the AQMEII3 model evaluation framework

2017 ◽  
Author(s):  
Efisio Solazzo ◽  
Christian Hogrefe ◽  
Augustin Colette ◽  
Marta Garcia-Vivanco ◽  
Stefano Galmarini
Keyword(s):  
Wind Speed ◽  
North America ◽  
Boundary Conditions ◽  
Time Scale ◽  
Model Evaluation ◽  
Evaluation Framework ◽  
Diagnostic Methods ◽  
Base Case ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions

Abstract. The work here complements the overview analysis of the modelling systems participating in the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3) by focusing on the performance for hourly surface ozone by two modelling systems, Chimere for Europe and CMAQ for North America. The evaluation strategy outlined in the course of the three phases of the AQMEII activity, aimed to build up a diagnostic methodology for model evaluation, is pursued here and novel diagnostic methods are proposed. In addition to evaluating the base case simulation in which all model components are configured in their standard mode, the analysis also makes use of sensitivity simulations in which the models have been applied by altering and/or zeroing lateral boundary conditions, emissions of anthropogenic precursors, and ozone dry deposition. To help understand of the causes of model deficiencies, the error components (bias, variance, and covariance) of the base case and of the sensitivity runs are analysed in conjunction with time-scale considerations and error modelling using the available error fields of temperature, wind speed, and NOx concentration. The results reveal the effectiveness and diagnostic power of the methods devised (which remains the main scope of this study), allowing the detection of the time scale and the fields that the two models are most sensitive to. The representation of planetary boundary layers (PBL) dynamics is pivotal to both models. In particular: i) The fluctuations slower than −1.5 days account for 70–85 % of the total ozone quadratic error; ii) A recursive, systematic error with daily periodicity is detected, responsible for 10–20 % of the quadratic total error; iii) Errors in representing the timing of the daily transition between stability regimes in the PBL are responsible for a covariance error as large as 9 ppb (as much as the standard deviation of the network-average ozone observations in summer in both Europe and North America); iv) The CMAQ ozone error has a weak/negligible dependence on the errors in NO2 and wind speed, while the error in NO2 significantly impacts the ozone error produced by Chimere; v) On a continent wide monitoring network-average, a zeroing out of anthropogenic emissions produces an error increase of 45 % (25 %) during summer and of 56 % (null) during winter for Chimere (CMAQ), while a zeroing out of lateral boundary conditions results in an ozone error increase of 30 % during summer and of 180 % during winter (CMAQ).

scholarly journals CTDAS-Lagrange v1.0: A high-resolution data assimilation system for regional carbon dioxide observations

2017 ◽  
Author(s):  
Wei He ◽  
Ivar R. van der Velde ◽  
Arlyn E. Andrews ◽  
Colm Sweeney ◽  
John Miller ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Transport Model ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions ◽  
Sensitivity Tests ◽  
Lagrange System ◽  
Data Assimilation System ◽  
Flux Adjustment ◽  
Assimilation System

Abstract. We have implemented a regional carbon dioxide data assimilation system based on the CarbonTracker Data Assimilation Shell (CTDAS) and a high-resolution Lagrangian transport model, the Stochastic Time-Inverted Lagrangian Transport model driven by the Weather Forecast and Research meteorological fields (WRF-STILT). With this system, named as CTDAS‑Lagrange, we simultaneously optimize terrestrial biosphere fluxes and four parameters that adjust the lateral boundary conditions (BCs) against CO2 observations from the NOAA ESRL North America tall tower and aircraft Programmable Flask Packages (PFPs) sampling program. Least-squares optimization is performed with a time-stepping ensemble Kalman smoother, over a time window of 10 days and assimilating sequentially a time series of observations. Because the WRF-STILT footprints are pre-computed, it is computationally efficient to run the CTDAS-Lagrange system. To estimate the uncertainties of the optimized fluxes from the system, we performed sensitivity tests with various a priori biosphere fluxes (SiBCASA, SiB3, CT2013B) and BCs (optimized mole fraction fields from CT2013B and CTE2014, and an empirical data set derived from aircraft observations), as well as with a variety of choices on the ways that fluxes are adjusted (additive or multiplicative), covariance length scales, biosphere flux covariances, BC parameter uncertainties, and model-data mismatches. In pseudo-data experiments, we show that in our implementation the additive flux adjustment method is more flexible in optimizing NEE than the multiplicative flux adjustment method, and that the CTDAS-Lagrange system has the ability to correct for the potential biases in the lateral boundary conditions and to resolve large biases in the prior biosphere fluxes. Using real observations, we have derived a range of estimates for the optimized carbon fluxes from a series of sensitivity tests, which places the North American carbon sink for the year 2010 in a range from −0.92 to −1.26 PgC/yr. This is comparable to the TM5-based estimates of CarbonTracker (version CT2016, −0.91 ± 1.10 PgC/yr) and CarbonTracker Europe (version CTE2016, −0.91 ± 0.31 PgC/yr). We conclude that CTDAS-Lagrange can offer a versatile and computationally attractive alternative to these global systems for regional estimates of carbon fluxes, which can take advantage of high-resolution Lagrangian footprints that are increasingly easy to obtain.

Evaluation of the new high resolution unstructured grid Marmara Sea model

2021 ◽  
Author(s):  
Mehmet Ilicak ◽  
Ivan Federico ◽  
Ivano Barletta ◽  
Nadia Pinardi ◽  
Stefania Angela Ciliberti ◽  
...  
Keyword(s):  
High Resolution ◽  
Boundary Conditions ◽  
Black Sea ◽  
Mixed Layer ◽  
Unstructured Grid ◽  
The Black Sea ◽  
Marmara Sea ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions ◽  
Forecasting System

<p>Marmara Sea including Bosphorus and Dardanelles Straits (i.e. Turkish Strait Systems, TSS) is the connection between the Black Sea and the Mediterranean. The exchange flow that occurs in the Straits is crucial to set the deep water properties in the Black Sea and the surface water conditions in the Northern Aegean Sea. We have developed a new high-resolution unstructured grid model (U-TSS) for the Marmara Sea including the Bosporus and Dardanelles Straits using the System of HydrodYnamic Finite Element Modules (SHYFEM). Using an unstructured grid in the horizontal better resolves geometry of the Turkish Straits. The new model has a resolution between 500 meter in the deep to 50 meter in the shallow areas, and 93 geopotential coordinate levels in the vertical. We conducted a 4 year hindcast simulation between 2016 and 2019 using lateral boundary conditions from CMEMS (https://marine.copernicus.eu/) analysis, in particular Black Sea Forecasting System (BS-FS) for the northern boundary and Mediterranean Sea Forecasting System (MS-FS) for the southern boundary. Atmospheric boundary conditions fare from the ECMWF dataset.</p><p>Mean averaged surface circulation shows that there is a cyclonic gyre in the middle of the basin due to Bosphorus jet flowing to the south and turning to west after reaching the southern Marmara coast. The U-TSS model has been validated against the seasonal in situ observations obtained from four different cruises between 2017 and 2018. The maximum bias occurs at around halocline depth between 20 to 30 meters.  We also found that root mean square error field is higher in the mixed layer interface. We conclude that capturing shallow mixed layer depth is very in the Marmara Sea due to the interplay of air-sea fluxes and mixing parametrizations uncertainties. Maximum salinity bias and rms in the new U-TSS model are around 3 psu which is a significant improvement with respect to previous studies. This new model will be used as an operational forecasting system and will provide lateral boundary conditions for the BS-FS and MS-FS models in the future.</p>

scholarly journals Soil Field Model Interoperability: Challenges and Impact on Screen Temperature Forecast Skill during the Nordic Winter

2012 ◽  
Vol 13 (4) ◽  
pp. 1215-1232 ◽  
Author(s):  
Jørn Kristiansen ◽  
Dag Bjørge ◽  
John M. Edwards ◽  
Gabriel G. Rooney
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Boundary Conditions ◽  
Limited Area ◽  
Lateral Boundary ◽  
Error Source ◽  
Main Error ◽  
Lateral Boundary Conditions ◽  
The Impact ◽  
Grid Length

Abstract The high-resolution (4-km grid length) Met Office (UKMO) Unified Model forecasts driven by the coarser-resolution (8-km grid length) High-Resolution Limited-Area Model (HIRLAM), UM4, often produce significantly colder screen-level (2 m) temperatures in winter over Norway than forecast with HIRLAM itself. To diagnose the main error source of this cold bias this study focuses on the forecast initial and lateral boundary conditions, particularly the initialization of soil moisture and temperature. The soil variables may be used differently by land surface schemes of varying complexity, representing a challenge to model interoperability. In a set of five experiments, daily UM4 forecasts are driven by alternating initial and lateral boundary conditions from two different parent models: HIRLAM and Met Office North Atlantic and Europe (NAE). The experiment period is November 2007. Points for scientific examination into the topics of model interoperability and sensitivity to soil initial conditions are identified. The soil moisture is the main error source and is therefore important also in winter, rather than being a challenge only in summer. The day-to-day variability in the forecast error is large with the larger errors on days with strong longwave heat loss at the surface (i.e., the forecast sensitivity to soil moisture content is significant but variable). The much drier soil in HIRLAM compared to NAE reduces the heat capacity of the soil layers and affects the heat flux from the surface soil layer to the surface. Normalizing the respective soil moisture fields reduces these differences. The impact of ground snow is quite limited.

scholarly journals Comparing Impacts of Satellite Data Assimilation and Lateral Boundary Conditions on Regional Model Forecasting: A Case Study of Hurricane Sandy (2012)

2017 ◽  
Vol 32 (2) ◽  
pp. 595-608
Author(s):  
Tong Zhu ◽  
Sid Ahmed Boukabara ◽  
Kevin Garrett
Keyword(s):  
Data Assimilation ◽  
Boundary Conditions ◽  
Satellite Data ◽  
Regional Model ◽  
Hurricane Sandy ◽  
Track Forecast ◽  
Lateral Boundary ◽  
Lateral Boundary Conditions ◽  
Outer Domain ◽  
The Impact

Abstract The impacts of both satellite data assimilation (DA) and lateral boundary conditions (LBCs) on the Hurricane Weather Research and Forecasting (HWRF) Model forecasts of Hurricane Sandy 2012 were assessed. To investigate the impact of satellite DA, experiments were run with and without satellite data assimilated, as well as with all satellite data but excluding Geostationary Operational Environmental Satellite (GOES) Sounder data. To gauge the LBC impact, these experiments were also run with a variety of outer domain (D-1) sizes. The inclusion of satellite DA resulted in analysis fields that better characterized the tropical storm structures including the warm core anomaly and wavenumber-1 asymmetry near the eyewall, and also served to reduce the forecast track errors for Hurricane Sandy. The specific impact of assimilating the GOES Sounder data showed positive impacts on forecasts of the storm minimum sea level pressure. Increasing the D-1 size resulted in increases in the day 4/5 forecast track errors when verified against the best track and the Global Forecast System (GFS) forecast, which dominated any benefits from assimilating an increased volume of satellite observations due to the larger domain. It was found that the LBCs with realistic environmental flow information could provide better constraints on smaller domain forecasts. This study demonstrated that satellite DA can improve the analysis of a hurricane asymmetry, especially in a shear environment, and then lead to a better track forecast, and also emphasized the importance of the LBCs and the challenges associated with the evaluation of satellite data impacts on regional model prediction.

Impact of Lateral Boundary Conditions on Regional Analyses

2017 ◽  
Vol 145 (4) ◽  
pp. 1361-1379 ◽  
Author(s):  
Kamel Chikhar ◽  
Pierre Gauthier
Keyword(s):  
Data Assimilation ◽  
Boundary Conditions ◽  
Large Scale ◽  
Climate Model ◽  
Systematic Errors ◽  
Global Climate Models ◽  
Lateral Boundary ◽  
Time Frequency ◽  
Lateral Boundary Conditions ◽  
Regional Analyses

Abstract Regional and global climate models are usually validated by comparison to derived observations or reanalyses. Using a model in data assimilation results in a direct comparison to observations to produce its own analyses that may reveal systematic errors. In this study, regional analyses over North America are produced based on the fifth-generation Canadian Regional Climate Model (CRCM5) combined with the variational data assimilation system of the Meteorological Service of Canada (MSC). CRCM5 is driven at its boundaries by global analyses from ERA-Interim or produced with the global configuration of the CRCM5. Assimilation cycles for the months of January and July 2011 revealed systematic errors in winter through large values in the mean analysis increments. This bias is attributed to the coupling of the lateral boundary conditions of the regional model with the driving data particularly over the northern boundary where a rapidly changing large-scale circulation created significant cross-boundary flows. Increasing the time frequency of the lateral driving and applying a large-scale spectral nudging significantly improved the circulation through the lateral boundaries, which translated in a much better agreement with observations.

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