Interannual variability of carbon monoxide emission estimates over South America from 2006 to 2010
Hooghiemstra, P.B. ; Krol, M.C. ; Leeuwen, T.T. van; Werf, G.R. van der; Novelli, P.C. ; Deeter, M.N. ; Aben, I. ; Rockmann, T. - \ 2012
Journal of Geophysical Research: Atmospheres 117 (2012). - ISSN 2169-897X
variational data assimilation - land-use change - climate-change - co emissions - amazon deforestation - brazilian amazon - fire emissions - model tm5 - mopitt - inversion
We present the first inverse modeling study to estimate CO emissions constrained by both surface and satellite observations. Our 4D-Var system assimilates National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) surface and Measurements Of Pollution In The Troposphere (MOPITT) satellite observations jointly by fitting a bias correction scheme. This approach leads to the identification of a positive bias of maximum 5 ppb in MOPITT column-averaged CO mixing ratios in the remote Southern Hemisphere (SH). The 4D-Var system is used to estimate CO emissions over South America in the period 2006-2010 and to analyze the interannual variability (IAV) of these emissions. We infer robust, high spatial resolution CO emission estimates that show slightly smaller IAV due to fires compared to the Global Fire Emissions Database (GFED3) prior emissions. South American dry season (August and September) biomass burning emission estimates amount to 60, 92, 42, 16 and 93 Tg CO/yr for 2006 to 2010, respectively. Moreover, CO emissions probably associated with pre-harvest burning of sugar cane plantations in Sao Paulo state are underestimated in current inventories by 50-100%. We conclude that climatic conditions (such as the widespread drought in 2010) seem the most likely cause for the IAV in biomass burning CO emissions. However, socio-economic factors (such as the growing global demand for soy, beef and sugar cane ethanol) and associated deforestation fires, are also likely as drivers for the IAV of CO emissions, but are difficult to link directly to CO emissions.
Comparing optimized CO emission estimates using MOPITT or NOAA surface network observations
Hooghiemstra, P.B. ; Krol, M.C. ; Bergamaschi, P. ; Laat, A.T.J. de; Werf, G.R. van der; Novelli, P.C. ; Deeter, M.N. ; Aben, I. ; Rockmann, T. - \ 2012
Journal of Geophysical Research: Atmospheres 117 (2012). - ISSN 2169-897X - 23 p.
variational data assimilation - zoom model tm5 - carbon-monoxide - tropospheric chemistry - inversion - validation - sciamachy - algorithm - pollution - aircraft
This paper compares two global inversions to estimate carbon monoxide (CO) emissions for 2004. Either surface flask observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) or CO total columns from the Measurement of Pollution in the Troposphere (MOPITT) instrument are assimilated in a 4D-Var framework. Inferred emission estimates from the two inversions are consistent over the Northern Hemisphere (NH). For example, both inversions increase anthropogenic CO emissions over Europe (from 46 to 94 Tg CO/yr) and Asia (from 222 to 420 Tg CO/yr). In the Southern Hemisphere (SH), three important findings are reported. First, due to their different vertical sensitivity, the stations-only inversion increases SH biomass burning emissions by 108 Tg CO/yr more than the MOPITT-only inversion. Conversely, the MOPITT-only inversion results in SH natural emissions (mainly CO from oxidation of NMVOCs) that are 185 Tg CO/yr higher compared to the stations-only inversion. Second, MOPITT-only derived biomass burning emissions are reduced with respect to the prior which is in contrast to previous (inverse) modeling studies. Finally, MOPITT derived total emissions are significantly higher for South America and Africa compared to the stations-only inversion. This is likely due to a positive bias in the MOPITT V4 product. This bias is also apparent from validation with surface stations and ground-truth FTIR columns. Our results show that a combined inversion is promising in the NH. However, implementation of a satellite bias correction scheme is essential to combine both observational data sets in the SH.
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities
Liu, Y. ; Weerts, A. ; Clark, M. ; Franssen, H.J. ; Moradkhani, S. ; Seo, D.J. ; Schwanenberg, D. ; Smith, P. ; Dijk, A.I.J.M. van; Velzen, N. ; He, M. ; Lee, H. ; Noh, S.J. ; Rakovec, O. ; Restrepo, P. - \ 2012
Hydrology and Earth System Sciences 16 (2012). - ISSN 1027-5606 - p. 3863 - 3887.
ensemble kalman filter - variational data assimilation - numerical weather-prediction - soil-moisture retrievals - land data assimilation - stochastic hydrometeorological model - sequential data assimilation - improving runoff prediction - state-parameter estimat
Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.
The importance of transport model uncertainties for the estimation of CO2 sources and sinks using satellite measurements
Houweling, S. ; Aben, I. ; Breon, F.M. ; Chevallier, F. ; Deutscher, N. ; Engelen, R. ; Gerbig, C. ; Griffith, D. ; Hungershoefer, K. ; Macatangay, R. ; Marshall, J. ; Notholt, J. ; Peters, W. ; Serrar, S. - \ 2010
Atmospheric Chemistry and Physics 10 (2010)20. - ISSN 1680-7316 - p. 9981 - 9992.
klimaatverandering - broeikasgassen - kooldioxide - monitoring - remote sensing - climatic change - greenhouse gases - carbon dioxide - monitoring - remote sensing - variational data assimilation - atmospheric co2 - source inversions - space - performance - fluxes - errors
This study presents a synthetic model intercomparison to investigate the importance of transport model errors for estimating the sources and sinks of CO2 using satellite measurements. The experiments were designed for testing the potential performance of the proposed CO2 lidar A-SCOPE, but also apply to other space borne missions that monitor total column CO2. The participating transport models IFS, LMDZ, TM3, and TM5 were run in forward and inverse mode using common a priori CO2 fluxes and initial concentrations. Forward simulations of column averaged CO2 (xCO2) mixing ratios vary between the models by s=0.5 ppm over the continents and s=0.27 ppm over the oceans. Despite the fact that the models agree on average on the sub-ppm level, these modest differences nevertheless lead to significant discrepancies in the inverted fluxes of 0.1 PgC/yr per 106 km2 over land and 0.03 PgC/yr per 106 km2 over the ocean. These transport model induced flux uncertainties exceed the target requirement that was formulated for the A-SCOPE mission of 0.02 PgC/yr per 106 km2, and could also limit the overall performance of other CO2 missions such as GOSAT. A variable, but overall encouraging agreement is found in comparison with FTS measurements at Park Falls, Darwin, Spitsbergen, and Bremen, although systematic differences are found exceeding the 0.5 ppm level. Because of this, our estimate of the impact of transport model uncerainty is likely to be conservative. It is concluded that to make use of the remote sensing technique for quantifying the sources and sinks of CO2 not only requires highly accurate satellite instruments, but also puts stringent requirements on the performance of atmospheric transport models. Improving the accuracy of these models should receive high priority, which calls for a closer collaboration between experts in atmospheric dynamics and tracer transport
Assimilation of remotely sensed latent heat flux in a distributed hydrological model
Schuurmans, J.M. ; Troch, P.A.A. ; Veldhuizen, A.A. ; Bastiaanssen, W.G.M. ; Bierkens, M.F.P. - \ 2003
Advances in Water Resources 26 (2003)2. - ISSN 0309-1708 - p. 151 - 159.
hydrologie van stroomgebieden - waterbalans - hydrologie - gegevens verzamelen - remote sensing - modellen - drenthe - catchment hydrology - water balance - hydrology - data collection - remote sensing - models - drenthe - variational data assimilation - moisture profile retrieval - soil-moisture - surface - radiobrightness
This paper addresses the question of whether remotely sensed latent heat flux estimates over a catchment can be used to improve distributed hydrological model water balance computations by the process of data assimilation. The data used is a series of satellite images for the Drentse Aa catchment in the Netherlands for the year 1995. These 1×1 km resolution images are converted into latent heat flux estimates using (urface nergy alance lgorithm for and [J Hydrol 2000;229:87]). The physically-based distributed model (ulation of undwater flow and surface water levels [J Hydrol 1997;192:158]) is used to compute the water balance of the Drentse Aa catchment for that same year. Comparison between model-derived and remotely sensed area-averaged evapotranspiration estimates show good agreement, but spatial analysis of the model latent heat flux estimates indicate systematic underestimation in areas with higher elevation. A constant gain Kalman filter data assimilation algorithm is used to correct the internal state variables of the distributed model whenever remotely sensed latent heat flux estimates are available. It was found that the spatial distribution of model latent heat flux estimates in areas with higher elevation were improved through data assimilation.