A wildland fire model with data assimilation

A wildfire model is formulated based on balance equations for energy and fuel, where the fuel loss due to combustion corresponds to the fuel reaction rate. The resulting coupled partial differential equations have coefficients that can be approximated from prior measurements of wildfires. An ensemble Kalman filter technique with regularization is then used to assimilate temperatures measured at selected points into running wildfire simulations. The assimilation technique is able to modify the simulations to track the measurements correctly even if the simulations were started with an erroneous ignition location that is quite far away from the correct one.Comment: 35 pages, 12 figures; minor revision January 2008. Original version available from http://www-math.cudenver.edu/ccm/report

The Distribution of Atmospheric Pollutants in Europe: Optimal Use of Models and Observations with a Data Assimilation Approach

The research activity presented in this manuscript deals with the implementation of a methodology to merge in an optimal way atmospheric modelling and observations at different spatial scales. In particular, we approached the problem of assimilation of ground measurements and satellite columnar data and how the Data Assimilation (DA) could improve the chemical transport model (CTMs) and correct biases and errors in the chemical species forecast. The work focused on tropospheric ozone and the species linked to its formation, since they play a crucial role in chemical processes during photochemical pollution events. The study was carried out implementing and applying an Optimal Interpolation (OI) DA technique in the air quality model BOLCHEM and the CHIMERE CTM. The OI routine was chosen because it has given satisfactory results in air quality modelling and because it is relatively simple and computationally inexpensive. In the first part of the study we evaluated the improvement in the capability of regional model BOLCHEM to reproduce the distribution of tropospheric pollutants, using the assimilation of surface chemical observations. Among the many causes of uncertainties of CTMs simulations, a particular focus is given by uncertainties in emissions, that are known to be high. The scientific purpose was to analyse the efficacy of DA in correcting the biases due to perturbed emission. The work was performed using an Observing System Simulation Experiment (OSSE), which allowed the quantification of assimilation impact, through comparison with a reference state. Different sensitivity tests were carried out in order to identify how assimilation can correct perturbations on O3, induced by NOx emissions biased in flux intensity and time. Tests were performed assimilating different species, varying assimilation time window length and starting hour of assimilation. Emissions were biased quantitatively up to ± 50% and shifted temporally up to ± 2 hours. The analysis brought to the conclusions that NO2 assimilation significantly improves O3 maxima during the assimilation, making it almost independent on different emission scenarios. The assimilation impact lasts up to 36-40 hours after the end of the assimilation window. This is a considerable result, especially when it is taken into account that DA generally yields significantly better forecasts in the 6-12 hours range, but improvements vanish afterwards. The NO2 night-time chemistry has the role of maintaining the correction of O3 due to assimilation also in the following day. Assimilating NO2 and O3 simultaneously bring to rather better results, although the benefit lasts only a few hours after the end of the assimilation window. It was found that the best results are achieved assimilating observations during the photochemically active period (06-18 UTC). It was also found that temporally biased NOx emissions only slightly perturb O3 concentration during the photochemically active regime, while the perturbation is larger during night-time. Assimilation has a very low impact during the assimilation window and a negligible impact after its end. The second part of PhD research activity dealt with the evaluation of the impact of assimilation of satellite NO2 tropospheric columns on the distribution of pollutants at the ground level during photochemical pollution events at continental scale. In particular, we focused on the assimilation of observations from SCIAMACHY and from OMI, and its effect on ozone in the lowermost troposphere in Europe. For an effective improvement in assimilated fields it is particularly important the consistency between satellite and model resolution. SCIAMACHY and OMI have a considerable difference in spatial and temporal resolution, allowing to test the role of data resolution on the effectiveness of assimilation. The role of data resolution on the effectiveness of assimilation was investigated also changing the model resolutions. It was found the perturbation on NO2 field due to assimilation causes a modification on ozone field that appears more spatially variable and higher in some photochemical polluted areas. Similar effects are detected both for SCIAMACHY and OMI assimilation. Significative effects of assimilation on ozone can be appreciate in polluted areas at local scale. Focusing on specific subdomains, it was found that the effect of assimilation lasts, in general, 8 hours and in few cases until the reactivation of active photochemical period in the following day. This is a strong impact, considering that assimilation is performed at most once a day and it is probably linked to the model underestimate of ozone and its precursors in polluted areas with respect to those measured by SCIAMACHY and OMI. In wide and highly polluted areas assimilation achieves satisfactory results, comparing simulated ground ozone with independent ground measurements. In that region where OMI assimilation in the coarse and fine resolution simulations and SCIAMACHY assimilation were confronted, we could conclude that these different assimilation set-up are almost similar. Whereas, in more localised polluted areas (i.e. comparable to model and satellite resolution), OMI assimilation in the finer resolution simulation performs better with respect to OMI assimilation in the coarse resolution simulation and SCIAMACHY assimilation. As a general conclusive statement, assimilation can be an important tool to make the spatial and temporal distribution of pollutants more realistic and closer to the specific local differences with the caveat of horizontal resolution of the assimilated columns and model simulations

Assimilation of trace gas retrievals with the Local Ensemble Transform Kalman Filter

Over the 50 year history of Numerical Weather Prediction (NWP), the focus has been on the modeling and prediction of meteorological parameters such as surface pressure, temperature, wind, and precipitation. However, due to concerns over pollution and to recent advancements in satellite technologies, an increasing number of NWP systems have been upgraded to include capabilities to analyze and predict the concentration of trace gases. This dissertation explores some of the specific issues that have to be addressed for an efficient modeling of the concentration of the trace gases. These issues include modeling the effects of convective mixing on the concentration of the trace gases and the multivariate assimilation of space-based observations of the concentration of the trace gases. In this dissertation, we assimilate observations of the concentration of trace gases with an implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation system on the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) NWP model. We use a modified version of the NCEP GFS model that was operational in 2004 at resolution T62/L28. We modify the model by adding parameterization for the process of convective mixing of the trace gases. We consider two specific trace gases: ozone (O3) and carbon monoxide (CO). We incorporate these gases into the model by using 3-dimensional time-dependent O3 and CO production-loss values from the Real-time Air Quality Modeling System (RAQMS) global chemical model. The O3 observations we assimilate are from the Solar Backscatter UltraViolet generation 2 (SBUV/2) satellite instrument (version 8) flown on the NOAA 16 and 17 satellites. The CO observations we assimilate are from the Measurements Of Pollution In The Troposphere (MOPITT) instrument (version 3) flown on the NASA TERRA satellite. We also develop a new observation operator for the assimilation of retrievals with the LETKF. We carry out numerical experiment for the period between 000UTC 1 July 2004 to 000UTC 15 August in the summer of 2004. The analysis and forecast impact of the assimilation of trace gas observations on the meteorological fields is assessed by comparing the analyses and forecasts to the high resolution operational NCEP GFS analyses and to radiosonde observations. The analysis and forecast impact on the trace gas fields is assessed by comparing the analyzed and predicted fields to observations collected during the Intercontinental Chemical Transport Experiment (INTEX-A) field mission. The INTEX-A field mission was conducted to characterize composition of pollution over North America, thus providing us with ozonesonde and aircraft based verification data. We find that adding the process of convective mixing to the parameterization package of the model and the assimilation of observations of the trace gases improves the analysis and forecast of the concentration of the trace gases. In particular, our system is more accurate in quantifying the concentration of O3 in the troposphere than the original NCEP GFS. Also, our system is competitive with the state-of-the-art RAQMS atmospheric chemical model in analyzing the concentration of O3 and CO throughout the full atmospheric model column. The assimilation of O3 and CO observations has a mixed impact on the analysis and forecast of the meteorological fields. We find that most of the negative impact on the meteorological fields can be eliminated, without much reduction to the positive impact on the trace gas fields, by inflating the prescribed variance of the trace gas observations. The appendices of this dissertation reproduces two papers on related research. The first paper covers the northward front movement and rising surface temperatures in the great planes. The second paper covers the assessment of predictability with a Local Ensemble Kalman Filter

Assimilation of ocean-colour plankton functional types to improve marine ecosystem simulations

We assimilated plankton functional types (PFTs) derived from ocean colour into a marine ecosystem model, to improve the simulation of biogeochemical indicators and emerging properties in a shelf sea. Error-characterized chlorophyll concentrations of four PFTs (diatoms, dinoflagellates, nanoplankton and picoplankton), as well as total chlorophyll for comparison, were assimilated into a physical-biogeochemical model of the North East Atlantic, applying a localized Ensemble Kalman filter. The reanalysis simulations spanned the years 1998 to 2003. The skill of the reference and reanalysis simulations in estimating ocean colour and in situ biogeochemical data were compared by using robust statistics. The reanalysis outperformed both the reference and the assimilation of total chlorophyll in estimating the ocean-colour PFTs (except nanoplankton), as well as the not-assimilated total chlorophyll, leading the model to simulate better the plankton community structure. Crucially, the reanalysis improved the estimates of not-assimilated in situ data of PFTs, as well as of phosphate and pCO2, impacting the simulation of the air-sea carbon flux. However, the reanalysis increased further the model overestimation of nitrate, in spite of increases in plankton nitrate uptake. The method proposed here is easily adaptable for use with other ecosystem models that simulate PFTs, for, e.g., reanalysis of carbon fluxes in the global ocean and for operational forecasts of biogeochemical indicators in shelf-sea ecosystems

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM

Reanalysis in Earth System Science: Towards Terrestrial Ecosystem Reanalysis

A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modelled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyses, and more in detail biogeochemical ocean and terrestrial reanalyses. In particular, we identify land surface, hydrologic and carbon cycle reanalyses which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics

On methods for assessment of the influence and impact of observations in convection-permitting numerical weather prediction

In numerical weather prediction (NWP), a large number of observations are used to create initial conditions for weather forecasting through a process known as data assimilation. An assessment of the value of these observations for NWP can guide us in the design of future observation networks, help us to identify problems with the assimilation system, and allow us to assess changes to the assimilation system. However, the assessment can be challenging in convection-permitting NWP. First, the strong nonlinearity in the forecast model limits the methods available for the assessment. Second, convection-permitting NWP typically uses a limited area model and provides short forecasts, giving problems with verification and our ability to gather sufficient statistics. Third, convection-permitting NWP often makes use of novel observations, which can be difficult to simulate in an observing system simulation experiment (OSSE). We compare methods that can be used to assess the value of observations in convection-permitting NWP and discuss operational considerations when using these methods. We focus on their applicability to ensemble forecasting systems, as these systems are becoming increasingly dominant for convection-permitting NWP. We also identify several future research directions: comparison of forecast validation using analyses and observations, the effect of ensemble size on assessing the value of observations, flow-dependent covariance localization, and generation and validation of the nature run in an OSSE.Comment: 35 page