Search

Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10- to 30-d lead times, an interval that is generally considered to be a predictability gap. The temporal correlation of rainfall forecasts is improved by up to 0.28 in this time range. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point toward a future of combining dynamical and data-driven forecasts for Earth system prediction.

The second West African Monsoon Modeling and Evaluation Project Experiment (WAMME II) is designed to improve understanding of the possible roles and feedbacks of sea surface temperature (SST), land use land cover change (LULCC), and aerosols forcings in the Sahel climate system at seasonal to decadal scales. The project's strategy is to apply prescribed observationally based anomaly forcing, i.e., "idealized but realistic" forcing, in simulations by climate models. The goal is to assess these forcings' effects in producing/amplifying seasonal and decadal climate variability in the Sahel between the 1950s and the 1980s, which is selected to characterize the great drought period of the last century. This is the first multi-model experiment specifically designed to simultaneously evaluate such relative contributions. The WAMME II models have consistently demonstrated that SST forcing is a major contributor to the 20th century Sahel drought. Under the influence of the maximum possible SST forcing, the ensemble mean of WAMME II models can produce up to 60% of the precipitation difference during the period. The present paper also addresses the role of SSTs in triggering and maintaining the Sahel drought. In this regard, the consensus of WAMME II models is that both Indian and Pacific Ocean SSTs greatly contributed to the drought, with the former producing an anomalous displacement of the Intertropical Convergence Zone (ITCZ) before the WAM onset, and the latter mainly contributes to the summer WAM drought. The WAMME II models also show that the impact of LULCC forcing on the Sahel climate system is weaker than that of SST forcing, but still of first order magnitude. According to the results, under LULCC forcing the ensemble mean of WAMME II models can produces about 40% of the precipitation difference between the 1980s and the 1950s. The role of land surface processes in responding to and amplifying the drought is also identified. The results suggest that catastrophic consequences are likely to occur in the regional Sahel climate when SST anomalies in individual ocean basins and in land conditions combine synergistically to favor drought.

An observing system simulation experiment (OSSE) is a modeling experiment used to evaluate the value of a new observing system. when actual observational data are not available. An OSSE system [...]

We examined the net terrestrial carbon flux to the atmosphere (FTA) simulated by nine models from the TRENDY dynamic global vegetation model project for its seasonal cycle and amplitude trend during 1961-2012. While some models exhibit similar phase and amplitude compared to atmospheric inversions, with spring drawdown and autumn rebound, others tend to rebound early in summer. The model ensemble mean underestimates the magnitude of the seasonal cycle by 40g% compared to atmospheric inversions. Global FTA amplitude increase (19g±g8g%) and its decadal variability from the model ensemble are generally consistent with constraints from surface atmosphere observations. However, models disagree on attribution of this long-term amplitude increase, with factorial experiments attributing 83g±g56g%, ĝ'3g±g74 and 20g±g30g% to rising CO2, climate change and land use/cover change, respectively. Seven out of the nine models suggest that CO2 fertilization is the strongest control - with the notable exception of VEGAS, which attributes approximately equally to the three factors. Generally, all models display an enhanced seasonality over the boreal region in response to high-latitude warming, but a negative climate contribution from part of the Northern Hemisphere temperate region, and the net result is a divergence over climate change effect. Six of the nine models show that land use/cover change amplifies the seasonal cycle of global FTA: some are due to forest regrowth, while others are caused by crop expansion or agricultural intensification, as revealed by their divergent spatial patterns. We also discovered a moderate cross-model correlation between FTA amplitude increase and increase in land carbon sink (R2 Combining double low line g0.61). Our results suggest that models can show similar results in some benchmarks with different underlying mechanisms; therefore, the spatial traits of CO2 fertilization, climate change and land use/cover changes are crucial in determining the right mechanisms in seasonal carbon cycle change as well as mean sink change.

Satellite observations are indispensable for weather forecasting, climate change monitoring, and environmental studies. Understanding and quantifying errors and uncertainties associated with satellite observations are essential for hardware calibration, data assimilation, and developing environmental and climate data records. Satellite observation errors can be classified into four categories: measurement, observation operator, representativeness, and preprocessing errors. Current methods for diagnosing observation errors still yield large uncertainties due to these complex errors. When simulating satellite errors, empirical errors are usually used, which do not always accurately represent the truth. We address these challenges by developing an error inventory simulator, the Satellite Error Representation and Realization (SatERR). SatERR can simulate a wide range of observation errors, from instrument measurement errors to model assimilation errors. Most of these errors are based on physical models, including existing and newly developed algorithms. SatERR takes a bottom-up approach: errors are generated from root sources and forward propagate through radiance and science products. This is different from, but complementary to, the top-down approach of current diagnostics, which inversely solves unknown errors. The impact of different errors can be quantified and partitioned, and a ground-truth testbed can be produced to test and refine diagnostic methods. SatERR is a community error inventory, open-source on GitHub, which can be expanded and refined with input from engineers, scientists, and modelers. This debut version of SatERR is centered on microwave sensors, covering traditional large satellites and small satellites operated by NOAA, NASA, and EUMETSAT. [ABSTRACT FROM AUTHOR]

Ensemble Forecast Sensitivity to Observation (EFSO) is a technique that can efficiently identify the beneficial/detrimental impacts of every observation in ensemble‐based data assimilation (DA). While EFSO has been successfully employed on atmospheric DA, it has never been applied to ocean or coupled DA due to the lack of a suitable error norm for oceanic variables. This study introduces a new density‐based error norm incorporating sea temperature and salinity forecast errors, making EFSO applicable to ocean DA for the first time. We implemented the oceanic EFSO on the CFSv2‐LETKF and investigated the impact of ocean observations under a weakly coupled DA framework. By removing the detrimental ocean observations detected by EFSO, the CFSv2 forecasts were significantly improved, showing the validation of impact estimation and the great potential of EFSO to be extended as a data selection criterion. Plain Language Summary: This study introduces a new method to efficiently estimate the beneficial/detrimental impacts of each oceanic observation in the air‐sea coupled data assimilation (CDA) by incorporating the forecast errors for sea temperature and salinity with a novel density norm. For the first time, we implemented this approach onto the operational‐like CDA system and examined the impacts of ocean observations on the National Centers for Environmental Prediction CFSv2 forecasts. Our findings demonstrate that assimilating ocean profiles and satellite‐retrieved sea surface temperature products significantly improves the CFSv2 forecasts. Moreover, after excluding the detrimental ocean observations identified by our new method, we observed significant improvements in the CFSv2 forecasts. This result validates the accuracy of impact estimation and underscores its great potential as a data selection criterion in CDA. Key Points: A new oceanic Ensemble Forecast Sensitivity to Observation (EFSO) is proposed and implemented on the CFSv2 to investigate observation impactsThe 24‐hr CFSv2 forecasts of sea temperature, salinity, and 2‐m air temperature were improved by 1.5%, 0.8%, and 0.4%, respectively, after removing detrimental ocean observations detected by EFSOThe improvements in the CFSv2 forecast can last for at least 5 days for the ocean and 3 days for the low‐level atmosphere [ABSTRACT FROM AUTHOR]

These models range from stylized mathematical models [[4]], system dynamics models [[5]], and agent-based models [[6]], to complex integrated assessment models (IAMs) [[7]] and coupled component models [[8]] such as the Earth system models [[9]]. The Earth is a very large and complex system that consists of human and natural components interacting bidirectionally with each other, thus forming coupled human and natural systems (CHANS) [[1]]. B Advances in understanding CHANS. b The increasing research efforts on CHANS at multiple scales [[11]], including theoretical, empirical, and modeling studies, enhance understanding of CHANS components, system behaviors, and feedback mechanisms. [Extracted from the article]

Atmospheric inversion of high spatiotemporal surface CO2 fllux without dynamic constraints and sufficient observations is an ill-posed problem, and a priori flux from a "bottom-up" estimation is commonly used in "top-down" inversion systems for regularization purposes. Ensemble Kalman filter-based inversion algorithms usually weigh a priori flux to the background or directly replace the background with the a priori flux. However, the "bottom-up" flux estimations, especially the simulated terrestrial-atmosphere CO2 exchange, are usually systematically biased at different spatiotemporal scales because of the deficiencies in understanding of some underlying processes. Here, we introduced a novel regularization algorithm into the Carbon in Ocean--Land--Atmosphere (COLA) data assimilation system, which assimilates a priori information as a unique observation (AAPO). The a priori information is not limited to "bottom-up" flux estimation. With the comprehensive assimilation regularization approach, COLA can apply the spatial gradient of the "bottom-up" flux estimation as a priori information to reduce the bias impact and enhance the dynamic information concerning the a priori "bottom-up" flux estimation. Benefiting from the enhanced signal-to-noise ratio in the spatial gradient, the global, regional, and grided flux estimations using the AAPO algorithm are significantly better than those obtained by the traditional regularization approach, especially over highly uncertain tropical regions in the context of observing simulation system experiments (OSSEs). We suggest that the AAPO algorithm can be applied to other greenhouse gas (e.g., CH4, NO2) and pollutant data assimilation studies. [ABSTRACT FROM AUTHOR]

The increase in amplitude of the atmospheric carbon dioxide cycle over the past fifty years can be attributed in part to the intensification of agriculture in the Northern Hemisphere. Agricultural advance increases atmospheric CO.sub.2 seasonality The atmospheric CO.sub.2 record displays a seasonal cycle reflecting seasonal variations in CO.sub.2 uptake by terrestrial vegetation. An increase in the amplitude of this seasonal cycle over the past five decades cannot be fully explained at present. Two groups now report that the intensification of agriculture may have been a key contributor to the increase in atmospheric CO.sub.2 seasonal amplitude. Ning Zeng et al. used the VEGAS terrestrial biosphere model to show that enhanced mid-latitude agricultural productivity contributed 45% of the increasing amplitude of global net surface carbon fluxes for the period 1961 to 2010, compared to 29% from climate change and 26% from CO.sub.2 fertilization. Josh Gray et al. used crop production statistics from the UN Food and Agriculture Organization and a carbon accounting model to demonstrate that as much as a quarter of the observed change in atmospheric CO.sub.2 seasonality can be explained by elevated crop productivity, with maize, wheat, rice and soybean major contributors. These studies will contribute to a better understanding of the global carbon cycle, and highlight the extent to which human actions are changing large-scale biosphere-atmosphere interactions. The atmospheric carbon dioxide (CO.sub.2) record displays a prominent seasonal cycle that arises mainly from changes in vegetation growth and the corresponding CO.sub.2 uptake during the boreal spring and summer growing seasons and CO.sub.2 release during the autumn and winter seasons.sup.1,2,3,4. The CO.sub.2 seasonal amplitude has increased over the past five decades, suggesting an increase in Northern Hemisphere biospheric activity.sup.2,5,6. It has been proposed that vegetation growth may have been stimulated by higher concentrations of CO.sub.2 as well as by warming in recent decades, but such mechanisms have been unable to explain the full range and magnitude of the observed increase in CO.sub.2 seasonal amplitude.sup.2,6,7,8,9,10,11,12,13. Here we suggest that the intensification of agriculture (the Green Revolution, in which much greater crop yield per unit area was achieved by hybridization, irrigation and fertilization) during the past five decades is a driver of changes in the seasonal characteristics of the global carbon cycle. Our analysis of CO.sub.2 data and atmospheric inversions shows a robust 15 per cent long-term increase in CO.sub.2 seasonal amplitude from 1961 to 2010, punctuated by large decadal and interannual variations. Using a terrestrial carbon cycle model that takes into account high-yield cultivars, fertilizer use and irrigation, we find that the long-term increase in CO.sub.2 seasonal amplitude arises from two major regions: the mid-latitude cropland between 25° N and 60° N and the high-latitude natural vegetation between 50° N and 70° N. The long-term trend of seasonal amplitude increase is 0.311 [plus or minus] 0.027 per cent per year, of which sensitivity experiments attribute 45, 29 and 26 per cent to land-use change, climate variability and change, and increased productivity due to CO.sub.2 fertilization, respectively. Vegetation growth was earlier by one to two weeks, as measured by the mid-point of vegetation carbon uptake, and took up 0.5 petagrams more carbon in July, the height of the growing season, during 2001-2010 than in 1961-1970, suggesting that human land use and management contribute to seasonal changes in the CO.sub.2 exchange between the biosphere and the atmosphere., Author(s): Ning Zeng [sup.1] , Fang Zhao [sup.1] , George J. Collatz [sup.2] , Eugenia Kalnay [sup.1] , Ross J. Salawitch [sup.1] , Tristram O. West [sup.3] , Luis Guanter [...]

We assessed different coupled data assimilation strategies with a hierarchy of coupled models, ranging from the simple coupled Lorenz model to the state-of-the-art coupled general circulation model CFSv2. With the coupled Lorenz model, we assessed the analysis accuracy by strongly-coupled Ensemble Kalman Filter (EnKF) and 4D-Variational (4D-Var) methods with varying assimilation window lengths. The analysis accuracy of the strongly-coupled EnKF with a short assimilation window is comparable to that of 4D-Var with a long assimilation window. For 4D-Var, the strongly-coupled approach with the coupled model produces more accurate ocean analysis than the ECCO-like approach using the uncoupled ocean model. Experiments with the coupled quasi-geostrophic model conclude that the strongly-coupled approach outperforms the weakly-coupled and uncoupled approaches for both the full-rank EnKF and 4D-Var, with the strongly-coupled EnKF and 4D-Var showing a similar level of accuracy higher than other coupled data assimilation approaches such as the outer loop coupling. A strongly-coupled EnKF software framework is developed and applied to the intermediatecomplexity coupled model SPEEDY-NEMO and the state-of-the-art operational coupled model CFSv2. Experiments assimilating synthetic or real atmospheric observations into the ocean through strongly-coupled EnKF show that the strongly-coupled approach improves the analysis of the atmosphere and upper oceans, but degrades observation fits in the deep ocean, probably due to the unreliable error correlation estimated by a small ensemble. The correlation-cutoff method is developed to reduce the unreliable error correlations between physically irrelevant model states and observations. Experiments with the coupled Lorenz model demonstrate that strongly-coupled EnKF informed by the correlation-cutoff method produces more accurate coupled analyses than the weakly-coupled and plain strongly-coupled EnKF regardless of the ensemble size. To extend the correlation-cutoff method to operational coupled models, a neural network approach is proposed to systematically acquire the observation localization functions for all pairs between the model state and observation types. The following strongly-coupled EnKF experiments with an intermediate-complexity coupled model show promising results with this method. [ABSTRACT FROM AUTHOR]

Atmospheric inversion of carbon dioxide (CO 2) measurements to better understand carbon sources and sinks has made great progress over the last 2 decades. However, most of the studies, including a four-dimensional variational ensemble Kalman filter and Bayesian synthesis approaches, directly obtain only fluxes, while CO 2 concentration is derived with the forward model as part of a post-analysis. Kang et al. (2012) used the local ensemble transform Kalman filter (LETKF), which updates the CO 2 , surface carbon flux (SCF), and meteorology fields simultaneously. Following this track, a system with a short assimilation window and a long observation window was developed (Liu et al., 2019). However, this data assimilation system faces the challenge of maintaining carbon mass conservation. To overcome this shortcoming, here we apply a constrained ensemble Kalman filter (CEnKF) approach to ensure the conservation of global CO 2 mass. After a standard LETKF procedure, an additional assimilation is used to adjust CO 2 at each model grid point and to ensure the consistency between the analysis and the first guess of the global CO 2 mass. Compared to an observing system simulation experiment without mass conservation, the CEnKF significantly reduces the annual global SCF bias from ∼ 0.2 to less than 0.06 Gt and slightly improves the seasonal and annual performance over tropical and southern extratropical regions. We show that this system can accurately track the spatial distribution of annual mean SCF. And the system reduces the seasonal flux root mean square error from a priori to analysis by 48 %–90 %, depending on the continental region. Moreover, the 2015–2016 El Niño impact is well captured with anomalies mainly in the tropics. [ABSTRACT FROM AUTHOR]

Localization is an essential technique for ensemble-based data assimilations (DAs) to reduce sampling errors due to limited ensembles. Unlike traditional distance-dependent localization, the correlation cutoff method (Yoshida and Kalnay, 2018; Yoshida, 2019) tends to localize the observation impacts based on their background error correlations. This method was initially proposed as a variable localization strategy for coupled systems, but it can also can be utilized extensively as a spatial localization. This study introduced and examined the feasibility of the correlation cutoff method as an alternative spatial localization with the local ensemble transform Kalman filter (LETKF) preliminary on the Lorenz (1996) model. We compared the accuracy of the distance-dependent and correlation-dependent localizations and extensively explored the potential of the hybrid localization strategies. Our results suggest that the correlation cutoff method can deliver comparable analysis to the traditional localization more efficiently and with a faster DA spin-up. These benefits would become even more pronounced under a more complicated model, especially when the ensemble and observation sizes are reduced. [ABSTRACT FROM AUTHOR]

The purpose of the present study is to use a new method of empirical model error correction, developed by Danforth et al. in 2007, based on estimating the systematic component of the nonperiodic errors linearly dependent on the anomalous state. The method uses singular value decomposition (SVD) to generate a basis of model errors and states. It requires only a time series of errors to estimate covariances and uses negligible additional computation during a forecast integration. As a result, it should be suitable for operational use at a relatively small computational expense. The method is tested with the Lorenz '96 coupled system as the truth and an uncoupled version of the same system as a model. The authors demonstrate that the SVD method explains a significant component of the effect that the model's unresolved state has on the resolved state and shows that the results are better than those obtained with Leith's empirical correction operator. The improvement is attributed to the fact that the SVD truncation effectively reduces sampling errors. Forecast improvements of up to 1000% are seen when compared with the original model. The improvements come at the expense of weakening ensemble spread.

The impact of different surface vegetations on long-term surface temperature change is estimated by subtracting reanalysis trends in monthly surface temperature anomalies from observation trends over the last four decades. This is done using two reanalyses, namely, the 40-yr ECMWF (ERA-40) and NCEPNCAR I (NNR), and two observation datasets, namely, Climatic Research Unit (CRU) and Global Historical Climate Network (GHCN). The basis of the observation minus reanalysis (OMR) approach is that the NNR reanalysis surface fields, and to a lesser extent the ERA-40, are insensitive to surface processes associated with different vegetation types and their changes because the NNR does not use surface observations over land, whereas ERA-40 only uses surface temperature observations indirectly, in order to initialize soil temperature and moisture. As a result, the OMR trends can provide an estimate of surface effects on the observed temperature trends missing in the reanalyses. The OMR trends obtained from observation minus NNR show a strong and coherent sensitivity to the independently estimated surface vegetation from normalized difference vegetation index (NDVI). The correlation between the OMR trend and the NDVI indicates that the OMR trend decreases with surface vegetation, with a correlation < -0.5, indicating that there is a stronger surface response to global warming in arid regions, whereas the OMR response is reduced in highly vegetated areas. The OMR trend averaged over the desert areas (0 < NDVI < 0.1) shows a much larger increase of temperature (~0.4[degrees]C [decade.sup.-1]) than over tropical forest areas (NDVI > 0.4) where the OMR trend is nearly zero. Areas of intermediate vegetation (0.1 < NDVI < 0.4), which are mostly found over midlatitudes, reveal moderate OMR trends (approximately 0.1[degrees]4).3[degrees]C [decade.sup.-1]). The OMR trends are also very sensitive to the seasonal vegetation change. While the OMR trends have little seasonal dependence over deserts and tropical forests, whose vegetation state remains rather constant throughout the year, the OMR trends over the midlatitudes, in particular Europe and North America, exhibit strong seasonal variation in response to the NDVI fluctuations associated with deciduous vegetation. The OMR trend rises up approximately to 0.2[degrees]-.3[degrees]C [decade.sup.-1] in winter and early spring when the vegetation cover is low, and is only 0.1[degrees]C [decade.sup.-1] in summer and early autumn with high vegetation. However, the Asian inlands (Russia, northern China with Tibet, and Mongolia) do not show this strong OMR variation despite their midlatitude location, because of the relatively permanent aridity of these regions.

The National Centers for Environmental Prediction (NCEP), Air Force Weather Agency (AFWA), Fleet Numerical Meteorology and Oceanography Center (FNMOC), National Weather Association, and American Meteorological Society (AMS) cosponsored a 'Symposium on the 50th Anniversary of Operational Numerical Weather Prediction,' on 14-17 June 2004 at the University of Maryland, College Park in College Park, Maryland. Operational numerical weather prediction (NWP) in the United States started with the Joint Numerical Weather Prediction Unit (JNWPU) on 1 July 1954, staffed by members of the U.S. Weather Bureau, the U.S. Air Force and the U.S. Navy. The origins of NCEP, AFWA, and FNMOC can all be traced to the JNWPU. The symposium celebrated the pioneering developments in NWP and the remarkable improvements in forecast skill and support of the nation's economy, well being, and national defense achieved over the last 50 years. This essay was inspired by the presentations from that symposium.

In this paper, the spatiotemporally changing nature of predictability is studied in a reduced-resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), a state-of-the-art numerical weather prediction model. Atmospheric predictability is assessed in the perfect model scenario for which forecast uncertainties are entirely due to uncertainties in the estimates of the initial states. Uncertain initial conditions (analyses) are obtained by assimilating simulated noisy vertical soundings of the 'true' atmospheric states with the local ensemble Kalman filter (LEKF) data assimilation scheme. This data assimilation scheme provides an ensemble of initial conditions. The ensemble mean defines the initial condition of 5-day deterministic model forecasts, while the time-evolved members of the ensemble provide an estimate of the evolving forecast uncertainties. The observations are randomly distributed in space to ensure that the geographical distribution of the analysis and forecast errors reflect predictability limits due to the model dynamics and are not affected by inhomogeneities of the observational coverage. Analysis and forecast error statistics are calculated for the deterministic forecasts. It is found that short-term forecast errors tend to grow exponentially in the extratropics and linearly in the Tropics. The behavior of the ensemble is explained by using the ensemble dimension (E dimension), a spatiotemporally evolving measure of the evenness of the distribution of the variance between the principal components of the ensemble-based forecast error covariance matrix. It is shown that in the extratropics the largest forecast errors occur for the smallest E dimensions. Since a low value of the E dimension guarantees that the ensemble can capture a large portion of the forecast error, the larger the forecast error the more certain that the ensemble can fully capture the forecast error. In particular, in regions of low E dimension, ensemble averaging is an efficient error filter and the ensemble spread provides an accurate prediction of the upper bound of the error in the ensemble-mean forecast.

The potential use of chaos synchronization techniques in data assimilation for numerical weather prediction models is explored by coupling a Lorenz three-variable system that represents 'truth' to another that represents 'the model.' By adding realistic 'noise' to observations of the master system, an optimal value of the coupling strength was clearly identifiable. Coupling only the y variable yielded the best results for a wide range of higher coupling strengths. Coupling along dynamically chosen directions identified by either singular or bred vectors could improve upon simpler chaos synchronization schemes. Generalized synchronization (with the parameter r of the slave system different from that of the master) could be easily achieved, as indicated by the synchronization of two identical slave systems coupled to the same master, but the slaves only provided partial information about regime changes in the master. A comparison with a standard data assimilation technique, three-dimensional variational analysis (3DVAR), demonstrated that this scheme is slightly more effective in producing an accurate analysis than the simpler synchronization scheme. Higher growth rates of bred vectors from both the master and the slave anticipated the location and size of error spikes in both 3DVAR and synchronization. With less frequent observations, synchronization using time-interpolated observational increments was competitive with 3DVAR. Adaptive synchronization, with a coupling parameter proportional to the bred vector growth rate, was successful in reducing episodes of large error growth. These results suggest that a hybrid chaos synchronization-data assimilation approach may provide an avenue to improve and extend the period for accurate weather prediction.

In 1997, during the late stages of production of NCEP-NCAR Global Reanalysis (GR), exploration of a regional reanalysis project was suggested by the GR project's Advisory Committee, 'particularly if the RDAS [Regional Data Assimilation System] is significantly better than the global reanalysis at capturing the regional hydrological cycle, the diurnal cycle and other important features of weather and climate variability.' Following a 6-yr development and production effort, NCEP's North American Regional Reanalysis (NARR) project was completed in 2004, and data are now available to the scientific community. Along with the use of the NCEP Eta model and its Data Assimilation System (at 32-km-45-1ayer resolution with 3-hourly output), the hallmarks of the NARR are the incorporation of hourly assimilation of precipitation, which leverages a comprehensive precipitation analysis effort, the use of a recent version of the Noah land surface model, and the use of numerous other datasets that are additional or improved compared to the GR. Following the practice applied to NCEP's GR, the 25-yr NARR retrospective production period (1979-2003) is augmented by the construction and daily execution of a system for near-real-time continuation of the NARR, known as the Regional Climate Data Assimilation System (R-CDAS). Highlights of the NARR results are presented: precipitation over the continental United States (CONUS), which is seen to be very near the ingested analyzed precipitation; fits of tropospheric temperatures and winds to rawinsonde observations; and fits of 2-m temperatures and 10-m winds to surface station observations. The aforementioned fits are compared to those of the NCEP-Department of Energy (DOE) Global Reanalysis (GR2). Not only have the expectations cited above been fully met, but very substantial improvements in the accuracy of temperatures and winds compared to that of GR2 are achieved throughout the troposphere. Finally, the numerous datasets produced are outlined and information is provided on the data archiving and present data availability.

The Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), and the Humidity Sounder for Brazil (HSB) form an integrated cross-track scanning temperature and humidity sounding system on the Aqua satellite of the Earth Observing System (EOS). AIRS is an infrared spectrometer/radiometer that covers the 3.7-15.4-[micro]m spectral range with 2378 spectral channels. AMSU is a 15-channel microwave radiometer operating between 23 and 89 GHz. HSB is a four-channel microwave radiometer that makes measurements between 150 and 190 GHz. In addition to supporting the National Aeronautics and Space Administration's interest in process study and climate research, AIRS is the first hyperspectral infrared radiometer designed to support the operational requirements for medium-range weather forecasting of the National Ocean and Atmospheric Administration's National Centers for Environmental Prediction (NCEP) and other numerical weather forecasting centers. AIRS, together with the AMSU and HSB microwave radiometers, will achieve global retrieval accuracy of better than 1 K in the lower troposphere under clear and partly cloudy conditions. This paper presents an overview of the science objectives, AIRS/AMSU/HSB data products, retrieval algorithms, and the ground-data processing concepts. The EOS Aqua was launched on May 4, 2002 from Vandenberg AFB, CA, into a 705-km-high, sun-synchronous orbit. Based on the excellent radiometric and spectral performance demonstrated by AIRS during prelaunch testing, which has by now been verified during on-orbit testing, we expect the assimilation of AIRS data into the numerical weather forecast to result in significant forecast range and reliability improvements. Index Terms--Climate, greenhouse gases, humidity sounder, hyperspectral, infrared, microwave, temperature sounder, weather forecasting.

Localization is an essential technique for ensemble-based data assimilations (DA) to reduce the sampling errors due to limited ensembles. Unlike traditional distance-dependent localization, the correlation cutoff method (Yoshida and Kalnay, 2018; Yoshida 2019) tends to localize the observation impacts based on their background error correlations. This method was initially proposed as a variable localization strategy for coupled systems, but it also can be extensively utilized as a spatial localization. This study introduced and examined the feasibility of the correlation cutoff method as an alternative spatial localization preliminary on the Lorenz (1996) model. We compared the accuracy of the distance-dependent and Abstract. Localization is an essential technique for ensemble-based data assimilations (DA) to reduce the sampling errors correlation-dependent localizations and extensively explored the potential of integrative localization strategies. Our results suggest that the correlation cutoff method can deliver comparable analysis to the traditional localization more efficiently and with a faster spin-up. These benefits would become even more pronounced under a more complicated model, especially when the ensemble and observation sizes are reduced. [ABSTRACT FROM AUTHOR]

Atmospheric inversion of carbon dioxide (CO2) measurements to understand carbon sources and sinks has made great progress over the last two decades. However, most of the studies, including four-dimension variational (4D-Var), Ensemble Kalman filter (EnKF), and Bayesian synthesis approaches, obtains directly only fluxes while CO2 concentration is derived with the forward model as post-analysis. Kang et al. (2012) used the Local Ensemble Transform Kalman Filter (LETKF) that updates the CO2, surface carbon fluxes (SCF), and meteorology field simultaneously. Following this track, a system with a short assimilation window and a long observation window was developed (Liu et al., 2019). However, this system faces the challenge of maintaining global carbon mass. To overcome this shortcoming, here we introduce a Constrained Ensemble Kalman Filter (CEnKF) approach to ensure the conservation of global CO2 mass. After a standard LETKF procedure, an additional assimilation process is applied to adjust CO2 at each model grid point and to ensure the consistency between the analysis and the first guess of global CO2 mass. In the context of observing system simulation experiments (OSSEs), we show that the CEnKF can significantly reduce the annual global SCF bias from ~0.2 gigaton to less than 0.06 gigaton by comparing between experiments with and without it. Moreover, the annual bias over most continental regions is also reduced. At the seasonal scale, the improved system reduced the flux root-mean-square error from priori to analysis by 48-90 %, depending on the continental region. Moreover, the 2015-2016 El Nino impact is well captured with anomalies mainly in the tropics. [ABSTRACT FROM AUTHOR]

One of the most significant impediments to progress in forecasting weather over North America is the relative paucity of routine observations over data-sparse regions adjacent to the United States. Prospectus Development Team Seven was convened to consider ways to promote research that seeks to determine implementations of observing systems that are optimal for weather prediction in the United States. An 'optimal' measurement system is considered to be one that maximizes the ratio of societal benefit to overall cost. The thrust of the conclusions is that existing means of estimating the value of current observing systems and the potential benefits of new or proposed observing systems are underutilized. At the same time, no rational way exists for comparing the cost of observations across the spectrum of federal agencies responsible for measuring the atmosphere and ocean. The authors suggest that a rational procedure for configuring an observation system that is optimal for weather prediction would consist of the following steps. 1) Identify specific forecast problems arising from insufficient data. Examples might include hurricane landfall and intensity forecasts, 24-h forecasts of intense extratropical cyclones affecting the West Coast and Alaska, and medium-range forecasts of severe weather for all of North America. 2) Use contemporary modeling techniques, such as observing system simulation experiments, ensemble forecasting, and model adjoint-derived sensitivities, to delineate measurement requirements for each specific forecasting problem and identify candidate observing systems and data assimilation techniques that could be brought to bear on each problem. 3) Estimate the incremental forecast improvements that could plausibly result from the added or reconfigured data and the societal benefits that would accrue from such improvements. 4) Estimate the overall cost (to the nation, not to specific federal agencies) of obtaining the data by the various candidate techniques and the benefits that are projected to result. 5) Use standard cost-benefit analysis as a basis for deciding the optimal deployment of measuring systems. The authors believe that a rational approach to atmospheric measurement is critical to further improvements in weather prediction and that such improvements might very well be made within the current budget of routine observations, integrated across all of the responsible federal agencies. This document outlines a proposed strategy for rationalizing atmosphere observations in aid of weather prediction in the United States. The paper begins with a summary of recommendations.

On 7 December 1992, The National Meteorological Center (NMC) started operational ensemble forecasting. The ensemble forecast configuration implemented provides 14 independent forecasts every day verifying on days 1-10. In this paper we briefly review existing methods for creating perturbations for ensemble forecasting. We point out that a regular analysis cycle is a 'breeding ground' for fast-growing modes. Based on this observation, we devise a simple and inexpensive method to generate growing modes of the atmosphere. The new method, 'breeding of growing modes,' or BGM, consists of one additional, perturbed short-range forecast, introduced on top of the regular analysis in an analysis cycle. The difference between the control and perturbed six-hour (first guess) forecast is scaled back to the size of the initial perturbation and then reintroduced onto the new atmospheric analysis. Thus, the perturbation evolves along with the time-dependent analysis fields, ensuring that after a few days of cycling the perturbation field consists of a superposition of fast-growing modes corresponding to the contemporaneous atmosphere, akin to local Lyapunov vectors. The breeding cycle has been designed to model how the growing errors are 'bred' and maintained in a conventional analysis cycle through the successive use of short-range forecasts. The bred modes should thus offer a good estimate of possible growing error fields in the analysis. Results from extensive experiments indicate that ensembles of just two BGM forecasts achieve better results than much larger random Monte Carlo or lagged average forecast (LAF) ensembles. Therefore, the operational ensemble configuration at NMC is based on the BGM method to generate efficient initial perturbations. The only two methods explicitly designed to generate perturbations that contain fast-growing modes corresponding to the evolving atmosphere are the BGM and the method of Lorenz, which is based on the singular modes of the linear tangent model. This method has been adopted operationally at The European Centre for Medium-Range Forecasts (ECMWF) for ensemble forecasting. Both the BGM and the ECMWF methods seem promising, but since it has not yet been possible to compare in detail their operational performance we limit ourselves to pointing out some of their similarities and differences.

Hybrid systems have become the state of the art among data assimilation methods. These systems combine the benefits of two other systems that are traditionally used in operational weather forecasting: an ensemble-based system and a variational system. One of the most recently proposed hybrid approaches is called hybrid gain (HG). It obtains the final analysis as a linear combination of two analyses, assuming that the innovations (i.e. the forecast and the set of observations used) between the two data assimilation methods are identical. A perfect model experiment was performed using the HG in the SPEEDY model to show a new methodology to assign different weights to the two analyses, LETKF and 3D-Var in the generation of the final analysis. Our new approach uses, in the assignment of the weights, the ensemble spread, considered to be a measure of uncertainty in the LETKF. Thus, it is possible to use the estimation of the uncertainty of the analysis that the LETKF provides, to determine where the system should give more weight to the LETKF or the 3D-Var analysis. For this purpose, we define a geographically varying weighting factor alpha, which multiplies the 3D-Var analysis, as the normalised spread for each variable at each level. Then, (1-alpha), which decreases with increasing spread, becomes the factor that multiplies the LETKF analysis. The underlying mechanism of the spread–error relationship is explained using a toy model experiment. The results are very encouraging: the original HG and the new weighted HG analyses have similar high quality and are better than both 3D-Var and LETKF. However, the dynamically weighted HG analyses are significantly more balanced than the original HG analyses are, which has probably contributed to the consistently improved performance observed in the weighted HG, which increases with time throughout the 5-day forecasts. [ABSTRACT FROM AUTHOR]

Proactive quality control (PQC) is a fully flow dependent QC based on ensemble forecast sensitivity to observations (EFSO). Past studies showed in several independent cases that GFS forecasts can be improved by rejecting observations identified as detrimental by EFSO. However, the impact of cycling PQC in sequential data assimilation has, so far, only been examined using the simple Lorenz ’96 model. Using a low-resolution spectral GFS model that assimilates PrepBUFR (no radiance) observations with the local ensemble transform Kalman filter (LETKF), this study aims to become a bridge between a simple model and the implementation into complex operational systems. We demonstrate the major benefit of cycling PQC in a sequential data assimilation framework through the accumulation of improvements from previous PQC updates. Such accumulated PQC improvement is much larger than the ‘‘current’’ PQC improvement that would be obtained at each analysis cycle using ‘‘future’’ observations. As a result, it is unnecessary to use future information, and hence this allows the operational implementation of cycling PQC. The results show that the analyses and forecasts are improved the most by rejecting all the observations identified as detrimental by EFSO, but that major improvements also come from rejecting just the most detrimental 10% observations. The forecast improvements brought by PQC are observed throughout the 10 days of integration and provide more than a 12-h forecast lead-time gain. An important finding is that PQC not only reduces substantially the root-mean-squared forecast errors but also the forecast biases. We also show a case of ‘‘skill dropout,’’ where the control forecast misses a developing baroclinic instability, whereas the accumulated PQC corrections result in a good prediction. [ABSTRACT FROM AUTHOR]

The NOAA Science Advisory Board appointed a task force to prepare a white paper on the use of observing system simulation experiments (OSSEs). Considering the importance and timeliness of this topic and based on this white paper, here we briefly review the use of OSSEs in the United States, discuss their values and limitations, and develop five recommendations for moving forward: national coordination of relevant research efforts, acceleration of OSSE development for Earth system models, consideration of the potential impact on OSSEs of deficiencies in the current data assimilation and prediction system, innovative and new applications of OSSEs, and extension of OSSEs to societal impacts. OSSEs can be complemented by calculations of forecast sensitivity to observations, which simultaneously evaluate the impact of different observation types in a forecast model system. [ABSTRACT FROM AUTHOR]