Advances in Meteorology

Volume 2018, Article ID 3438501, 10 pages

https://doi.org/10.1155/2018/3438501

## ASCAT Wind Superobbing Based on Feature Box

Academy of Ocean Science and Engineering, National University of Defense Technology, Changsha 410073, China

Correspondence should be addressed to Weimin Zhang; moc.931@401gnahzmw

Received 19 September 2017; Accepted 8 February 2018; Published 22 April 2018

Academic Editor: Stefania Bonafoni

Copyright © 2018 Boheng Duan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Redundant observations impose a computational burden on an operational data assimilation system, and assimilation using high-resolution satellite observation data sets at full resolution leads to poorer analyses and forecasts than lower resolution data sets, since high-resolution data may introduce correlated error in the assimilation. Thus, it is essential to thin the observations to alleviate these problems. Superobbing like other data thinning methods lowers the effect of correlated error by reducing the data density. Besides, it has the added advantage of reducing the uncorrelated error through averaging. However, thinning method using averaging could lead to the loss of some meteorological features, especially in extreme weather conditions. In this paper, we offer a new superobbing method which takes into consideration the meteorological features. The new method shows very good error characteristic, and the numerical simulation experiment of typhoon “Lionrock” (2016) shows that it has a positive impact on the analysis and forecast compared to the traditional superobbing.

#### 1. Introduction

The demand for more accurate predictions of hurricanes is increasing in order to issue timely warning to society. This will help to minimize losses and damage. One primary objective is to enhance the observation targeting and observability of hurricanes. Satellite observations can effectively compensate for the shortcomings of traditional methods of sea surface measurement and provide all-weather observation over the sea surface, which is of great significance to improve the numerical prediction of strong convective weather in the marine area [1].

The spaceborne scatterometer observes the backscattering caused by the sea surface roughness, and then the sea surface wind can be retrieved. Scatterometer data were first used in a numerical weather forecasting operational system in 1998, when the European Center for Medium-Range Weather Forecasts (ECMWF) incorporated ERS-1 scatterometer data into its global three-dimensional variational system [2]. Previous studies have shown that scatterometer data have significant impacts on weather forecasting and climate monitoring [3–9]. Particularly, it has been demonstrated to be useful in the prediction of tropical cyclones (Isaksen and Stoffelen, 2000) [5] and extratropical cyclones [4]. ASCAT surface wind data have been used in many daily weather forecast operations such as the ECMWF, the United Kingdom’s National Weather Service (Met Office), the National Weather Service of France (Meteo-France), and Environment Canada. In July 2009, the Japan Meteorological Agency (JMA) began to use ASCAT data for the Global Spectrum Model (GSM) and found that the ASCAT wind can capture the development of the low-pressure system and improve the prediction precision. Hersbach (2010) pointed out that the neutral wind retrieved by ASCAT had a positive effect on the ECMWF forecasting system [10]. In 2011, Bi et al. evaluated the role of the ASCAT wind in the global data assimilation system of the NCEP (National Centers for Environmental Prediction); the results showed that ocean surface wind of ASCAT has a positive effect on the forecast of wind and temperature [11].

Current satellite observations generally have high temporal and spatial resolution. For example, the horizontal grid size of the ASCAT scatterometer has reached 12.5 km. If the high-resolution observations are directly brought into the assimilation system, it will greatly increase the computational overhead. In addition, high-resolution data will inevitably produce some spatial correlation errors in the observations [12]. Therefore, the thinning technique of observations becomes a key technology of pretreatment in actual satellite assimilation. It plays an important role in improving data assimilation effect, and different thinning algorithms should be designed for different types of satellite observations. At present, the common way of satellite observations thinning is using a temporal or spatial sampling method, which makes observations distributed evenly in time and space, or using a “super-observation” method, where the values of observation minus background or innovations are averaged within a certain region and assigned to the background chosen as superob. The superob has the advantage of reducing both the correlated error and the uncorrelated error of the observation (Howard, 2004) [13]. Ochotta et al. (2006) proposed two thinning methods [12]: one is to cluster observation data according to the observed spatial position and observed data and finally to keep the center of each cluster; the other approach is to iterate over the most redundant observations from the data set. Li et al. (2010) [14] proposed a thinning scheme combining the background error covariance of the model, which minimizes the analysis error variance by selecting observations. Bauer et al. (2011) [15] proposed a method based on singular vectors to find sensitive regions of the satellite observations. The nonsensitive regions use the conventional thinning method, while the sensitive regions keep more observations. Gratton et al. (2015) [16] proposed a thinning method based on hierarchical observations, starting with the lowest (sparse) layer and adding observations gradually based on a posteriori error estimate. However, the above methods have no special consideration for the real-time meteorological feature of the observation. In this paper, based on the wind data of the ASCAT scatterometer, a new superobbing method which takes into consideration feature of the wind innovation field is proposed. To achieve the purpose of thinning and decorrelation, the main idea of this algorithm is to retain the spatial wind variability characteristics in dynamic situations, while at the same time the winds with low spatial variability are averaged over larger areas.

The structure of this paper is as follows: A brief introduction of superob is given in Section 2. Section 3 gives the specific flow of the wind superobbing using the feature box. In Section 4, we give the error characteristic of the superob using feature box and compare it to the regular superob. In Section 5, we use the typhoon forecast impact experiment to examine how this new method can affect the forecast, and the results are compared with those of the traditional superobbing scheme. Finally, the conclusion is given in Section 6.

#### 2. Wind Superobbing with Regular Box

In this section, we will give the specific definition of the superob and derive the expression for the observation error within a box. Before we begin to derive the expression of the superob, we give the following assumptions [13] in order to simplify the problem.(1)Observation and background errors are not correlated to each other.(2)All of the background errors within a box have the same magnitude and are fully correlated with each other.(3)All of the observation errors are constant and the spatial correlation is constant on the length scales of a box.(4)All of the innovations within a box are weighted equally.

By fixing the size of a 3-dimensional box (for ASCAT wind field, it is 2-dimensional), we got observations (where here is or component of the wind) and corresponding background in a box. Then the superob of a box can be formed as a weighted average of the observation minus the background; namely,where is the background value at the chosen superob location and is the weight assigned to each pair. Assume is the truth at location ; then , , and , where is the corresponding error at location . Then the equation can be rewritten as, , and are the error vectors of weights, observations, and backgrounds within the box. Squaring and averaging the error in equation over many boxes produce

According to assumption (1) , equation becomes

Since all of the innovations are weighted equally and according assumption (2), we havethus,

Based on assumption (3), can be written as the product of the observation error and a correlation matrix ; namely, , where

Define the uncorrelated observation error and the correlated error ; the superob error can be simplified to

Howard (2004) [13] pointed out that the uncorrelated part of the observation error can be approximated by the innovation variance within a box; namely, . Since one box makes up a sample, the innovation variance can be estimated using the standard statistical formula:

Thus, the superob error can be rewritten as

Although it does not provide the true error of the superob, it provides a reasonable estimate of the superob error with all the assumptions. It can be seen from the equation that the superob reduces the random error greatly within a box but not the correlated error. If the observations are perfectly uncorrelated, the superob error will only depend on the innovation variance within the box. Thus, the bigger the box is, the smaller the superob error would be. However, one major concern is that a box too big in size may lead to the loss of the meteorological features. Another problem is that box with big size would disobey the hypothesis that the spatial correlation of observation is constant within a box.

#### 3. Wind Superobbing with Feature Box

At present, the most commonly used thinning methods in data assimilation all use a fixed size of grid (2-dimension) or box (3-dimension). However, the global smoothing may usually destroy some of the structural characteristics of the data field, while these structural features often contain some key information, such as the wind field vortex structure of typhoons. Duan et al. (2017) [17] proposed a new thinning method called feature thinning; it preserves the characteristics of the wind field, while at the same time removing the redundancy of the observation. The grid size is flexible according to the spatial structure of the wind field and the feature here comes from the wind field itself. This paper will draw on the idea of the feature thinning to determine the box size of each superob. However, the feature in this paper is extracted from the innovation field but not the wind observation field, since data assimilation is a combination of observation and background. The flow of the superobbing with feature box algorithm is shown in Figure 1.