Design and analysis of an advanced thermal management system for the solar close observations and proximity experiments spacecraft

In this paper,the mission and the thermal environment of the Solar Close Observations and Proximity Experiments(SCOPE)spacecraft are analyzed,and an advanced thermal management system(ATMS)is designed for it.The relationship and functions of the integrated database,the intelligent thermal control system and the efficient liquid cooling system in the ATMS are elaborated upon.For the complex thermal field regulation system and extreme space thermal environment,a modular simulation and thermal field planning method are proposed,and the feasibility of the planning algorithm is verified by numerical simulation.A solar array liquid cooling system is developed,and the system simulation results indicate that the temperatures of the solar arrays meet the requirements as the spacecraft flies by perihelion and aphelion.The advanced thermal management study supports the development of the SCOPE program and provides a reference for the thermal management in other deep-space exploration programs.

Observation system simulation experiments using an ensemble-based method in the northeastern South China Sea

An ensemble-based method for the observation system simulation experiment(OSSE)is employed to design optimal observation stations and assess the present observation stations in the northeastern South China Sea(SCS).We employed the 20-year(1992-2012)sea surface height(SSH)data to design an array to monitor the intraseasonal to interannual variability.The results show that the most key region was found located at the northwest of Luzon Island(LI)where the energetic Luzon cyclonic gyre(LCG)occurs;other key regions include the edge of the LCG,the northwest of the Luzon Strait(LS),and the southwest of Taiwan,China.By contrast,we found that the present observation stations might oversample at the northwest of the LS and undersample at the northwest of LI.In addition,the optimal stations perform better in a larger area than the present stations.In vertical direction,the key layer is located within the upper 200-m depth,of which the surface and subsurface layers are most valuable to the observing system.

Objective array design for three-dimensional temperature and salinity observation:Application to the South China Sea

In this study,a moored array optimization tool(MAOT)was developed and applied to the South China Sea(SCS)with a focus on three-dimensional temperature and salinity observations.Application of the MAOT involves two steps:(1)deriving a set of optimal arrays that are independent of each other for different variables at different depths based on an empirical orthogonal function method,and(2)consolidating these arrays using a K-center clustering algorithm.Compared with the assumed initial array consisting of 17 mooring sites located on a 3°×3°horizontal grid,the consolidated array improved the observing ability for three-dimensional temperature and salinity in the SCS with optimization efficiencies of 19.03%and 21.38%,respectively.Experiments with an increased number of moored sites showed that the most cost-effective option is a total of 20 moorings,improving the observing ability with optimization efficiencies up to 26.54%for temperature and 27.25%for salinity.The design of an objective array relies on the ocean phenomenon of interest and its spatial and temporal scales.In this study,we focus on basin-scale variations in temperature and salinity in the SCS,and thus our consolidated array may not well resolve mesoscale processes.The MAOT can be extended to include other variables and multi-scale variability and can be applied to other regions.

An Economical Approach to Four-dimensional Variational Data Assimilation

Four-dimensional variational data assimilation （4DVar） is one of the most promising methods to provide optimal analysis for numerical weather prediction （NWP）. Five national NWP centers in the world have successfully applied 4DVar methods in their global NWPs, thanks to the increment method and adjoint technique. However, the application of 4DVar is still limited by the computer resources available at many NWP centers and research institutes. It is essential, therefore, to further reduce the computational cost of 4DVar. Here, an economical approach to implement 4DVar is proposed, using the technique of dimension- reduced projection （DRP）, which is called ＂DRP-4DVar.＂ The proposed approach is based on dimension reduction using an ensemble of historical samples to define a subspace. It directly obtains an optimal solution in the reduced space by fitting observations with historical time series generated by the model to form consistent forecast states, and therefore does not require implementation of the adjoint of tangent linear approximation. To evaluate the performance of the DRP-4DVar on assimilating different types of mesoscale observations, some observing system simulation experiments are conducted using MM5 and a comparison is made between adjoint-based 4DVar and DRP-4DVar using a 6-hour assimilation window.

Numerical Assessing Experiments on the Individual Component Impact of the Meteorological Observation Network on the "July 2000" Torrential Rain in Beijing

In an effort to assess the impact of the individual component of meteorological observations （ground-based CPS precipitable water vapor, automatic and conventional meteorological observations） on the torrential rain event in 4-5 July 2000 in Beijing （with the 24-h accumulated precipitation reaching 240 mm）, 24-h observation system experiments are conducted numerically by using the MM5/WRF 3DVAR system and the nonhydrostatic MM5 model. Results indicate that, because the non-conventional GPS observations are directly assimilated into the initial analyses by 3DVAR system, better initial fields and 24-h simulation for the severe precipitation event are achieved than those under the MM5/Litter_R objective analysis scheme. Further analysis also shows that the individual component of meteorological observation network plays their special positive role in the improvement of initial field analysis and forecasting skills. 3DVAR scheme with or without radiosonde and pilot observation has the most significant influence on numerical simulation, and automatic and conventional surface meteorological observations rank second. After acquiring the supplement information from the other meteorological observations, the ground-based GPS precipitable water vapor data can more obviously reflect initial field assimilation and precipitation forecast. By incorporating the groundbased CPS precipitable water vapor data into the 3DVAR analyses at the initial time, the threat scores （TS） with thresholds of 1, 5, 10, and 20 mm are increased by 1%-8% for 6- and 24-h accumulated precipitation observations, respectively. This work gives one helpful example that assesses the impact of individual component of the existing meteorological observation network on the high influence weather event using 3DVAR numerical system.

An Experimental Study on the Drag Property of High-Temperature Particles Falling into Cold Liquid Pool

This experiment is to study the special resistant induced by the high-speed evaporation surrounding themoving high-temperature particles. An observable equipment was designed, in which the first 11 experiments wereperformed by pouring one or several Zirconia spheres with various high-temperature and a diameter of 3～ 10 mminto a water pool. The particles falling-down speeds were recorded by high-speed photographic instrumentation,and pressures and water temperatures were measured. A comparison between the experiments with cold and hotspheres respectively, employing three different sphere types each, was presented. The experimental data, com-pared with the theory of the evaporation drag model, are nearly identical.

The REMO Ocean Data Assimilation System into HYCOM(RODAS_H):General Description and Preliminary Results

The first version of the Brazilian Oceano- graphic Modeling and Observation Network （REMO） ocean data assimilation system into the Hybrid Coordi- nate Ocean Model （HYCOM） （RODAS H） has recently been constructed for research and operational purposes. The system is based on a multivariate Ensemble Optimal Interpolation （EnOI） scheme and considers the high fre- quency variability of the model error co-variance matrix. The EnOl can assimilate sea surface temperature （SST）, satellite along-track and gridded sea level anomalies （SLA）, and vertical profiles of temperature （T） and salinity （S） from Argo. The first observing system experiment was carried out over the Atlantic Ocean （78°S-50°N, 100°W-20°E） with HYCOM forced with atmospheric reanalysis from 1 January to 30 June 2010. Five integra- tions were performed, including the control run without assimilation. In the other four, different observations were assimilated： SST only （A SST）; Argo T-S profiles only （AArgo）; along-track SLA only （A_SLA）; and all data employed in the previous runs （A_All）. The A_SST, A_Argo, and A_SLA runs were very effective in improv- ing the representation of the assimilated variables, but they had relatively little impact on the variables that were not assimilated. In particular, only the assimilation of S was able to reduce the deviation of S with respect to ob- servations. Overall, the A_All run produced a good analy- sis by reducing the deviation of SST, T, and S with respect to the control run by 39%, 18%, and 30%, respectively, and by increasing the correlation of SLA by 81%.

The Sensitive Regions Identified by CNOPs of Three Typhoon Events

In this paper, several sets of observing system simulation experiments (OSSEs) were designed for three typhoon cases to determine whether or not the additional observation data in the sensitive regions identified by conditional nonlinear optimal perturbations (CNOPs) could improve the short-range forecast of typhoons. The results show that the CNOPs capture the sensitive regions for typhoon forecasts, which implies that conducting additional observation in these specific regions and eliminating initial errors could reduce forecast errors. It is inferred from the results that dropping sondes in the CNOP sensitive regions could lead to improvements in typhoon forecasts.

A Review of Advances in Lightning Observations during the Past Decade in Guangdong, China

This paper reviews recent advances in understanding the physical processes of artificially triggered light- ning and natural lightning as well as the progress in testing lightning protection technologies, based on a series of lightning field campaigns jointly conducted by the Chinese Academy of Meteorological Sciences and Guangdong Meteorological Bureau since 2006. During the decade-long series of lightning field experi- ments, the technology of rocket-wire artificially triggered lightning has been improved, and has successfully triggered 94 lightning flashes. Through direct lightning current waveform measurements, an average return stroke peak current of 16 kA was obtained. The phenomenon that the downward leader connects to the lateral surface of the upward leader in the attachment process was discovered, and the speed of the upward leader during the connection process being significantly greater than that of the downward leader was re- vealed. The characteristics of several return strokes in cloud-to-ground lighting have also been unveiled, and the mechanism causing damage to lightning protection devices （i.e., ground potential rise within the rated current） was established. The performance of three lightning monitoring systems in Guangdong Province has also been quantitatively assessed.

Impact of Different Guidances on Sensitive Areas of Targeting Observations Based on the CNOP Method

The conditional nonlinear optimal perturbations（CNOPs） obtained by a fast algorithm are applied to determining the sensitive area for the targeting observation of Typhoon Matsa in 2005 using an operational regional prediction model-the Global/Regional Assimilation and PrEdiction System（GRAPES）.Through a series of sensitivity experiments,several issues on targeting strategy design are discussed,including the effectivity of different guidances to determine the sensitive area（or targeting area） and the impact of sensitive area size on improving the 24-h forecast.In this study,three guidances are used along with the CNOP to find sensitive area for improving the 24-h prediction of sea level pressure and accumulated rainfall in the verification region.The three guidances are based on winds only;on winds,geopotential height,and specific humidity;and on winds,geopotential height,specific humidity,and observation error,respectively.The distribution and effectivity of the sensitive areas are compared with each other,and the results show that the sensitive areas identified by the three guidances are different in terms of convergence and effectivity.All the sensitive areas determined by these guidances can lead to improvement of the 24-h forecast of interest. The second and third guidances are more effective and can identify more similar sensitive areas than the first one.Further,the size of sensitive areas is changed the same way for three guidances and the 24-h accumulated rainfall prediction is examined.The results suggest that a larger sensitive area can result in better prediction skill,provided that the guidance is sensitive to the size of sensitive areas.

A Fast Algorithm for Solving CNOP and Associated Target Observation Tests

Conditional Nonlinear Optimal Perturbation （CNOP） is a new method proposed by Mu et al. in 2003, which generalizes the linear singular vector （LSV） to include nonlinearity. It has become a powerful tool for studying predictability and sensitivity among other issues in nonlinear systems. This is because the CNOP is able to represent, while the LSV is unable to deal with, the fastest developing perturbation in a nonlinear system. The wide application of this new method, however, has been limited due to its large computational cost related to the use of an adjoint technique. In order to greatly reduce the computational cost, we hereby propose a fast algorithm for solving the CNOP based on the empirical orthogonal function （EOF）. The algorithm is tested in target observation experiments of Typhoon Matsa using the Global/Regional Assimilation and PrEdiction System （GRAPES）, an operational regional forecast model of China. The effectivity and feasibility of the algorithm to determine the sensitivity （target） area is evaluated through two observing system simulation experiments （OSSEs）. The results, as expected, show that the energy of the CNOP solved by the new algorithm develops quickly and nonlinearly. The sensitivity area is effectively identified with the CNOP from the new algorithm, using 24 h as the prediction time window. The 24-h accumulated rainfall prediction errors （ARPEs） in the verification region are reduced significantly compared with the ＂true state,＂ when the initial conditions （ICs） in the sensitivity area are replaced with the ＂observations.＂ The decrease of the ARPEs can be achieved for even longer prediction times （e.g., 72 h）. Further analyses reveal that the decrease of the 24-h ARPEs in the verification region is attributable to improved simulations of the typhoon＇s initial warm-core, upper level relative vorticity, water vapor conditions, etc., as a result of the updated ICs in the sensitivity area.

A review of landscape ecology experiments to understand ecological processes

Background:One way in which we make inferences about ecological processes is via experimentation.Many ecological processes happen at landscape extents and it is at this extent that experimentation is more challenging.This review explores the intersection between experimentation,ecological processes and landscape ecology.Specifically,this review seeks to discover how scientists design experiments to understand ecological processes at landscape scales.Results:I found 87 papers where these three concepts intersected,and reviewed them in more depth to assess characteristics of scale(treatment and study area extent),replication,research question and experiment type.Conclusions:The findings suggest that experimental approaches for understanding ecological processes are well established,and beginning to more readily accommodate spatial dimensions.However,there is room to integrate more spatially explicit,landscape-scale experiments into studies of ecological processes.