scholarly journals Review comments on "Regional CO2 inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0"

2019 ◽  
Author(s):  
Anonymous
Keyword(s):  
Inversion Algorithm ◽  
Modular Inversion

scholarly journals Regional CO<sub>2</sub> inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0

2021 ◽  
Vol 14 (6) ◽  
pp. 3383-3406
Author(s):  
Guillaume Monteil ◽  
Marko Scholze
Keyword(s):  
Inverse Modeling ◽  
Transport Model ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Model Errors ◽  
Inversion Algorithm ◽  
In Situ Data ◽  
Increasing Demand ◽  
Modular Inversion

Abstract. Atmospheric inversions are used to derive constraints on the net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. The resolution and accuracy that the fluxes can be estimated with depends, among other factors, on the quality and density of the observational coverage, on the precision and accuracy of the transport model used by the inversion to relate fluxes to observations, and on the adaptation of the statistical approach to the problem studied. In recent years, there has been an increasing demand from stakeholders for inversions at higher spatial resolution (country scale), in particular in the framework of the Paris agreement. This step up in resolution is in theory enabled by the growing availability of observations from surface in situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the bottom-up modeling community (vegetation models, fossil fuel emission inventories, etc.). However, it calls for new developments in the inverse models: diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency. In this context, we developed LUMIA, the Lund University Modular Inversion Algorithm. LUMIA is a Python library for inverse modeling built around the central idea of modularity: it aims to be a platform that enables users to construct and experiment with new inverse modeling setups while remaining easy to use and maintain. It is in particular designed to be transport-model-agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. We have constructed a first regional inversion setup using the LUMIA framework to conduct regional CO2 inversions in Europe using in situ data from surface and tall-tower observation sites. The inversions rely on a new offline coupling between the regional high-resolution FLEXPART Lagrangian particle dispersion model and the global coarse-resolution TM5 transport model. This test setup is intended both as a demonstration and as a reference for comparison with future LUMIA developments. The aims of this paper are to present the LUMIA framework (motivations for building it, development principles and future prospects) and to describe and test this first implementation of regional CO2 inversions in LUMIA.

scholarly journals An Efficient Hardware Implementation of Finite Field Inversion for Elliptic Curve Cryptography

2019 ◽  
Vol 8 (9) ◽  
pp. 827-832
Keyword(s):  
High Performance ◽  
Design Methodology ◽  
Hardware Implementation ◽  
Cmos Technology ◽  
Clock Frequency ◽  
Inversion Algorithm ◽  
Prime Field ◽  
Modular Inversion ◽  
Reduction In Area ◽  
Computing Speed

The modular inversion operation is an essential hardware design for computing speed when we use it in cryptography applications. Through this work, we present a FSM based design methodology to achieve speed, area and high-performance modular binary inversion algorithm over 256-bit prime field. The proposed architecture implemented using Xilinx Virtex-7 FPGA device, it achieves 37% reduction in area-delay product and 15% and 16% of improvement in speed and throughput respectively, when compared with existing designs. Also, ASIC based implementation is done using TSMC 65nm CMOS technology, the synthesis results achieved the maximum operating clock frequency is 833 MHz and throughput of 626Mbps, which makes it suitable for speed-critical cryptoapplications.

scholarly journals Regional CO<sub>2</sub> inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0

2019 ◽  
Author(s):  
Guillaume Monteil ◽  
Marko Scholze
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
Inverse Modelling ◽  
Current Status ◽  
Model Errors ◽  
Inversion Algorithm ◽  
New Developments ◽  
In Situ Data ◽  
Modular Inversion

Abstract. Atmospheric inversions are commonly used for estimating large-scale (continental to regional) net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. Recently, there has been an increasing demand from stakeholders for robust estimates of greenhouse gases at country-scale (or higher) resolution, in particular in the framework of the Paris agreement. This increase in resolution is in theory enabled by the growing availability of observations from surface in-situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the process-based (bottom-up) modelling community (vegetation models, fossil fuel emission inventories). This, however, calls for new developments in the inverse modelling systems, mainly in terms of diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency, We have developed the Lund University Modular Inversion Algorithm (LUMIA) as a tool to address some of these new developments. LUMIA is meant to be a platform for inverse modelling developments at Lund University. It aims at being a flexible, yet simple and easy to maintain set of tools that modellers can combine to build inverse modelling experiments. It is in particular designed to be transport model agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. Here, we briefly describe the motivations for developing LUMIA as well as the underlying development principles, current status and future prospects. We present a first LUMIA inversion setup for a regional CO2 inversions over Europe, based on a new coupling between the Lagrangian FLEXPART (high resolution foreground transport) and the global coarse resolution TM5 transport models, using in-situ data from surface and tall tower observation sites.

Research and Hardware Design of Scalable Dual-Field Montgomery Modular Inversion Algorithm

2011 ◽  
Vol 58-60 ◽  
pp. 1007-1012
Author(s):  
Lin Chen ◽  
Wan Zhong Sun ◽  
Xing Yuan Chen ◽  
Chao Zhou
Keyword(s):  
Elliptic Curve ◽  
Prime Number ◽  
Elliptic Curve Cryptography ◽  
Arithmetic Operation ◽  
Hardware Design ◽  
Operation Time ◽  
Hardware Architecture ◽  
Inversion Algorithm ◽  
Modular Inversion ◽  
Hardware Designs

The modular inversion is an essential and the most time consuming arithmetic operation in Elliptic Curve Cryptography (ECC). This paper presents an improved scalable dual-field Montgomery modular inversion algorithm which is based on Kaliski algorithm. It can support the prime number modular inversion algorithm and binary modular inversion algorithm. According to the algorithm, the hardware architecture is designed. The result of experimentation shows that the hardware design is superior to other hardware designs in operation time, circuit area and flexibility.

Fast GF (p) modular inversion algorithm suitable for VLSI implementation

2002 ◽  
Vol 38 (14) ◽  
pp. 706 ◽  
Author(s):  
Tao Zhou ◽  
Xingjun Wu ◽  
Guoqiang Bai ◽  
Hongyi Chen
Keyword(s):  
Vlsi Implementation ◽  
Inversion Algorithm ◽  
Modular Inversion

Design and implementation of dual-field modular inversion algorithm

2010 ◽  
Vol 27 (4) ◽  
pp. 531-537 ◽  
Author(s):  
Yang Xiaohui ◽  
Yang Tongjie ◽  
Dai Zibin ◽  
Zhang Yongfu
Keyword(s):  
Inversion Algorithm ◽  
Design And Implementation ◽  
Modular Inversion
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