Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics

We applied the parametric variance Kalman filter (PvKF) data assimilation designed in Part I of this two-part paper to GOSAT methane observations with the hemispheric version of CMAQ to obtain the methane field (i.e., optimized analysis) with its error variance. Although the Kalman filter computes error covariances, the optimality depends on how these covariances reflect the true error statistics. To achieve more accurate representation, we optimize the global variance parameters, including correlation length scales and observation errors, based on a cross-validation cost function. The model and the initial error are then estimated according to the normalized variance matching diagnostic, also to maintain a stable analysis error variance over time. The assimilation results in April 2010 are validated against independent surface and aircraft observations. The statistics of the comparison of the model and analysis show a meaningful improvement against all four types of available observations. Having the advantage of continuous assimilation, we showed that the analysis also aims at pursuing the temporal variation of independent measurements, as opposed to the model. Finally, the performance of the PvKF assimilation in capturing the spatial structure of bias and uncertainty reduction across the Northern Hemisphere is examined, indicating the capability of analysis in addressing those biases originated, whether from inaccurate emissions or modelling error.

Incremental Learning with Neural Network Algorithm for the Monitoring Pre-Convective Environments Using Geostationary Imager

Early warning of severe weather caused by intense convective weather systems is challenging. To help such activities, meteorological satellites with high temporal and spatial resolution have been utilized for the monitoring of instability trends along with water vapor variation. The current study proposes a retrieval algorithm based on an artificial neural network (ANN) model to quickly and efficiently derive total precipitable water (TPW) and convective available potential energy (CAPE) from Korea’s second geostationary satellite imagery measurements (GEO-KOMPSAT-2A/Advanced Meteorological Imager (AMI)). To overcome the limitations of the traditional static (ST) learning method such as exhaustive learning, impractical, and not matching in a sequence data, we applied an ANN model with incremental (INC) learning. The INC ANN uses a dynamic dataset that begins with the existing weight information transferred from a previously learned model when new samples emerge. To prevent sudden changes in the distribution of learning data, this method uses a sliding window that moves along the data with a window of a fixed size. Through an empirical test, the update cycle and the window size of the model are set to be one day and ten days, respectively. For the preparation of learning datasets, nine infrared brightness temperatures of AMI, six dual channel differences, temporal and geographic information, and a satellite zenith angle are used as input variables, and the TPW and CAPE from ECMWF model reanalysis (ERA5) data are used as the corresponding target values over the clear-sky conditions in the Northeast Asia region for about one year. Through the accuracy tests with radiosonde observation for one year, the INC NN results demonstrate improved performance (the accuracy of TPW and CAPE decreased by approximately 26% and 26% for bias and about 13% and 12% for RMSE, respectively) when compared to the ST learning. Evaluation results using ERA5 data also reveal more stable error statistics over time and overall reduced error distribution compared with ST ANN.

Uncertainty Propagation for Inertial Navigation with Coning, Sculling, and Scrolling Corrections

This paper investigates the propagation of estimation errors through a common coning, sculling, and scrolling architecture used in modern-day inertial navigation systems. Coning, sculling, and scrolling corrections often have an unaccounted for effect on the error statistics of inertial measurements used to describe the state and uncertainty propagation for position, velocity, and attitude estimates. Through the development of an error analysis for a set of coning, sculling, and scrolling algorithms, mappings of the measurement and estimation errors through the correction term are adaptively generated. Using the developed mappings, an efficient and consistent propagation of the state and uncertainty, within the multiplicative extended Kalman filter architecture, is achieved. Monte Carlo analysis is performed, and results show that the developed system has favorable attributes when compared to the traditional mechanization.

A statistical interpolation code for ocean analysis and forecasting

We present a data assimilation package for use with ocean circulation models in analysis, forecasting and system evaluation applications. The basic functionality of the package is centered on a multivariate linear statistical estimation for a given predicted/background ocean state, observations and error statistics. Novel features of the package include support for multiple covariance models, and the solution of the least squares normal equations either using the covariance matrix or its inverse - the information matrix. The main focus of this paper, however, is on the solution of the analysis equations using the information matrix, which offers several advantages for solving large problems efficiently. Details of the parameterization of the inverse covariance using Markov Random Fields are provided and its relationship to finite difference discretizations of diffusion equations are pointed out. The package can assimilate a variety of observation types from both remote sensing and in-situ platforms. The performance of the data assimilation methodology implemented in the package is demonstrated with a yearlong global ocean hindcast with a 1/4°ocean model. The code is implemented in modern Fortran, supports distributed memory, shared memory, multi-core architectures and uses Climate and Forecasts compliant Network Common Data Format for Input/Output. The package is freely available with an open source license from www.tendral.com/tsis/

Tropical cyclones prediction by numerical models in India Meteorological Department

lkj & bl 'kks/k&Ik= esa Hkkjr ekSle foKku foHkkx ¼Hkk- ekS- fo- fo-½ esa viukbZ xbZ pØokr izfr:fir djus dh dfYir rduhdksa ij ppkZ dh xbZ gSA vDrwcj 1999 esa mM+hlk esa vk, egkpØokr ds izkjfEHkd {ks=ksa esa dkYifud Hkzfeyrk dk mi;ksx djds] pØokr ds fof’k"V ekWMy] Doklh ySaxjfx;u ekWMy ¼D;w- ,y- ,e-½ ls 72 ?kaVs ds iwokZuqeku vkSj Hkkjr ekSle foKku foHkkx ds lhfer {ks= fun’kZ ¼,y- ,- ,e-½ ls 36 ?kaVs ds iwokZuqeku izfr:fir fd, x,A bl 'kks/k esa] 26 ls 28 vDrwcj rd dh izkjafHkd fLFkfr;ksa ds vk/kkj ij D;w- ,y- ,e- ls pØokr ds ekxZ ds iwokZuqeku dh vkSlr =qfV;k¡ 24 ?kaVs ds fy, 21 fd-eh-] 48 ?kaVs ds fy, 91 fd-eh- vkSj 72 ?kaVs ds fy, 179 fd-eh- jghA 1998&2004 rd ds fiNys lkr o"kksZa ds nkSjku D;w- ,y- ,e- ls pØokr ds ekxZ ds iwokZuqeku dh =qfV;ksa ds vk¡dM+ksa ij Hkh blesa ppkZ dh xbZ gSA blds vykok] ,y- ,- ,e- ls fd, x, iwokZuqeku ij izkjafHkd fLFkfr;ksa ds izHkko dh Hkh tk¡p dh xbZA fofHkUu izkjafHkd fLFkfr;ksa ls rS;kj fd, x, vkSlr ¼lesfdr½ iwokZuqeku ls 24 ?kaVs ds iwokZuqeku esa 123 fd-eh- vkSj 36 ?kaVs ds iwokZuqeku esa 81 fd-eh- dh =qfV;k¡ ikbZ xbZ] tks ,dek= iwokZuqeku dh rqyuk esa de jghA bu iz;ksxksa ls ;g irk pyk fd dkYifud Hkzfeyrk okys D;w- ,y- ,e- ekWMy ls pØokr ds ekxZ dk lVhd iwokZuqeku izkIr fd;k tk ldrk gS tks vHkh rd la[;kRed ekWMyksa ls miyC/k gks ikrk FkkA In the present paper, the cyclone bogusing techniques followed in India Meteorological Department (IMD) were discussed. Using the idealized vortex in the initial fields for Orissa super cyclone October 1999, the specialized cyclone model, Quasi-Lagrangian Model (QLM) 72 hours track forecast and also 36 hours forecast with IMD limited area model (LAM) were simulated. In this case, the QLM average track forecast errors based on 26-28 October initial conditions were 21 km for 24 hours, 91 km for 48 hours and 179 km for 72 hours. Also the QLM track forecast error statistics during the last 7 years 1998-2004 are discussed. In addition, the impact of initial conditions on the LAM forecast was examined. It was observed that the mean (ensemble) forecast generated from different initial conditions was shown track error of 123 km in 24 hours and 81 km in 36 hours forecast which is less than individual forecast. These experiments have established that the QLM model, with idealized vortex, provides track forecast within an accuracy level that was currently available from numerical models.

Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar using Artificial Neural Networking

Forecasting the compressive strength of concrete is a complex task owing to the interactions among concrete ingredients. In addition, an important characteristic of the concrete failure surface is its six-fold symmetry. In this study, an artificial neural network (ANN) and adaptive neuro fuzzy interface system (ANFIS) were employed to model the compressive strength of natural volcanic ash mortar (VAM) by using the six-fold symmetry of concrete failure. The modeling was correlated with four parameters. To train and test the projected models, data for more than 150 samples were collected from the literature. Furthermore, mortar samples with varying proportions of volcanic ash were prepared in the laboratory and tested, and the results were used to validate the models. The performance of the developed models was assessed using numerous statistical measures. The results show that both the ANN and ANFIS models accurately predict the compressive strength of VAM with R-square above 0.9 and lower error statistics. The permutation feature analysis confirmed that the age of specimens affects the strength of VAM the most, followed by the water-to-cement ratio, curing temperature, and percentage of volcanic ash.

Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments

We evaluate the global atmospheric methane column retrievals from the new TROPOMI satellite instrument and apply them to a global inversion of methane sources for 2019 at 2∘ × 2.5∘ horizontal resolution. We compare the results to an inversion using the sparser but more mature GOSAT satellite retrievals and to a joint inversion using both TROPOMI and GOSAT. Validation of TROPOMI and GOSAT with TCCON ground-based measurements of methane columns, after correcting for retrieval differences in prior vertical profiles and averaging kernels using the GEOS-Chem chemical transport model, shows global biases of −2.7 ppbv for TROPOMI and −1.0 ppbv for GOSAT and regional biases of 6.7 ppbv for TROPOMI and 2.9 ppbv for GOSAT. Intercomparison of TROPOMI and GOSAT shows larger regional discrepancies exceeding 20 ppbv, mostly over regions with low surface albedo in the shortwave infrared where the TROPOMI retrieval may be biased. Our inversion uses an analytical solution to the Bayesian inference of methane sources, thus providing an explicit characterization of error statistics and information content together with the solution. TROPOMI has ∼ 100 times more observations than GOSAT, but error correlation on the 2∘ × 2.5∘ scale of the inversion and large spatial inhomogeneity in the number of observations make it less useful than GOSAT for quantifying emissions at that scale. Finer-scale regional inversions would take better advantage of the TROPOMI data density. The TROPOMI and GOSAT inversions show consistent downward adjustments of global oil–gas emissions relative to a prior estimate based on national inventory reports to the United Nations Framework Convention on Climate Change but consistent increases in the south-central US and in Venezuela. Global emissions from livestock (the largest anthropogenic source) are adjusted upward by TROPOMI and GOSAT relative to the EDGAR v4.3.2 prior estimate. We find large artifacts in the TROPOMI inversion over southeast China, where seasonal rice emissions are particularly high but in phase with extensive cloudiness and where coal emissions may be misallocated. Future advances in the TROPOMI retrieval together with finer-scale inversions and improved accounting of error correlations should enable improved exploitation of TROPOMI observations to quantify and attribute methane emissions on the global scale.

Artificial Intelligence-Based Techniques for Rainfall Estimation Integrating Multisource Precipitation Datasets

This study presents a comprehensive investigation of multiple Artificial Intelligence (AI) techniques—decision tree, random forest, gradient boosting, and neural network—to generate improved precipitation estimates over the Upper Blue Nile Basin. All the AI methods merged multiple satellite and atmospheric reanalysis precipitation datasets to generate error-corrected precipitation estimates. The accuracy of the model predictions was evaluated using 13 years (2000–2012) of ground-based precipitation data derived from local rain gauge networks in the Upper Blue Nile Basin region. The results indicate that merging multiple sources of precipitation substantially reduced the systematic and random error statistics in the Upper Blue Nile Basin. The proposed methods have great potential in predicting precipitation over the complex terrain region.

Interpreting estimated observation error statistics of weather radar measurements using the ICON-LAM-KENDA system

Assimilation of weather radar measurements including radar reflectivity and radial wind data has been operational at the Deutscher Wetterdienst, with a diagonal observation error (OE) covariance matrix. For an implementation of a full OE covariance matrix, the statistics of the OE have to be a priori estimated, for which the Desroziers method has been often used. However, the resulted statistics consists of contributions from different error sources and are difficult to interpret. In this work, we use an approach that is based on samples for truncation error in radar observation space to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with the OE statistics estimated by the Desroziers method. It is found that the statistics of the RE help the understanding of several important features in the variances and correlation length scales of the OE for both reflectivity and radial wind data and the other error sources from the microphysical scheme, radar observation operator and the superobbing technique may also contribute, for instance, to differences among different elevations and observation types. The statistics presented here can serve as a guideline for selecting which observations are assimilated and for assignment of the OE covariance matrix that can be diagonal or full and correlated.

Performance Trade-Offs in Cyber–Physical Control Applications With Multi-Connectivity

Modern communication devices are often equipped with multiple wireless communication interfaces with diverse characteristics. This enables exploiting a form of multi-connectivity known as interface diversity to provide path diversity with multiple communication interfaces. Interface diversity helps to combat the problems suffered by single-interface systems due to error bursts in the link, which are a consequence of temporal correlation in the wireless channel. The length of an error burst is an essential performance indicator for cyber–physical control applications with periodic traffic, as this defines the period in which the control link is unavailable. However, the available interfaces must be correctly orchestrated to achieve an adequate trade-off between latency, reliability, and energy consumption. This work investigates how the packet error statistics from different interfaces impact the overall latency–reliability characteristics and explores mechanisms to derive adequate interface diversity policies. For this, we model the optimization problem as a partially observable Markov decision process (POMDP), where the state of each interface is determined by a Gilbert–Elliott model whose parameters are estimated based on experimental measurement traces from LTE and Wi-Fi. Our results show that the POMDP approach provides an all-round adaptable solution, whose performance is only 0.1% below the absolute upper bound, dictated by the optimal policy under the impractical assumption of full observability.