Global and regional ionosphere models using the GPS double difference phase observable-猫眼课题宝

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1996

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S. Schaer;G. Beutler;L. Mervart;M. Rothacher;U. Wild

国际全球定位系统地球动力学服务（IGS）的CODE分析中心每天使用双差相位观测值的无电离层线性组合产生轨道、地球定向参数、台站坐标和其他地球物理参数。干净（一）e. L1和L2相的无周跳）部分每天都很容易获得。以米为单位的差L1-L2仅包含差分电离层折射效应，并且在模糊度未解决的情况下，包含由于L1和L2中的初始载波相位模糊度而引起的恒定偏差。在这里，我们正是使用这个可观察到的IGS网络中提取电离层信息。一方面，在双差水平上使用差L1-L2是不理想的-差分显著地减少了电离层信号。另一方面，我们有一个干净的信号的优势。此外，处理被简化，因为卫星和接收器特定的偏差抵消了最大程度上在我们的方法。通常，我们用单层模型来模拟电离层总电子含量（TEC），该模型基于相应的映射函数。相对于早期的尝试（本地电离层模型使用泰勒级数展开的纬度和太阳固定经度），我们开发的垂直TEC到一系列的球谐函数。我们可以使用地心纬度和太阳固定经度或太阳地磁系统中的等效集合作为独立的参数。这些模型的优点-泰勒级数展开-是非常适合区域和全球模型。使用CODE分析中心一周的区域（欧洲）和全球数据（整个IGS网络）的第一个结果似乎表明，在正常的电离层条件下，电离层模型是非常有用的单频GPS用户，即。e.如果考虑这些TEC模型，电离层折射效应将大大降低。欧洲轨道确定中心在1995年IGS讲习班上提交的论文，波茨坦，德国，1995年5月15日至17日在通常只有单波段（L1）接收器可用的时间段内，重要的是深入了解由未建模的电离层折射在GPS网络中引入的偏差（Beutler等人，1988年）。后来，很明显，即使有双波段接收器，电离层折射的短周期变化也可能损害GPS分析（Beutler等人，1989年）。在后一份文件中，也有线索表明可以从双波段全球定位系统数据中提取关于电离层的宝贵信息。建模和监测电离层是博士的主要课题。（Wild，1994）。在这篇论文中，它可以表明，本地电离层模型，如（Georgiadiou和Kleusberg，1988年）提出的是非常有效的，以消除-或大大减少-单波段接收器的规模偏差工作在双波段接收器，其数据被用来建立一个本地电离层模型附近。(Wild，1994年）计算了这样的本地电离层模型的IGS网站在一个延长的时间段。他还介绍了一种评估全球定位系统台站附近电离层随机行为的程序。主要结论是，可以从IGS网络中提取有关电离层的基本信息。局部电离层模型已在许多场合证明其有用性。然而，在像IGS这样的网络中拥有与台站一样多的电离层模型的概念很难操作。（Wild，1994年）使用的建模技术必须在一个重要方面加以修改，才有可能用基于N个台站数据的一个区域或全球模型来取代N个局部模型。让我们简要回顾一下（Wild，1994）和下面使用的建模特性。怀尔德使用所谓的单层模型，假设所有的自由电子都集中在一个无限小厚度的壳层中。这个薄壳位于球形地球上方的高度H处。该理想层的高度H通常被设置为350或400千米，其大致对应于电离层的F区域中的电子密度分布的峰值高度。电子密度E -层的表面密度-被假定为地心纬度β和太阳固定经度s的函数

The CODE Analysis Center of the International GPS Service for Geodynamics (IGS) produces orbits, Earth orientation parameters, station coordinates, and other parameters of geophysical interest on a daily basis using the ionosphere-free linear combination of the double difference phase observables. Consequently, clean (i. e. cycle-slip-free) portions of the L1 and the L2 phases are readily available for every day. The difference L1–L2 in meters contains only differential ionospheric refraction effects and in the ambiguitiy-unresolved case a constant bias due to the initial carrier phase ambiguities in L1 and L2. Here we use exactly this observable to extract ionospheric information from the IGS network. On one hand it is not ideal to use the difference L1–L2 on the double difference level — the differencing reduces the ionospheric signal considerably. On the other hand we have the advantage of a clean signal. Also, processing is simplified because satellite and receiver specific biases cancel out to the greatest extent in our approach. As usual we model the ionospheric Total Electron Content (TEC) with a single-layer model which is based on the corresponding mapping function. As opposed to earlier attempts (local ionosphere models using Taylor series expansions in latitude and sun-fixed longitude) we develop the vertical TEC into a series of spherical harmonics. We may use the geocentric latitude and the sun-fixed longitude or an equivalent set in the solar-geomagnetic system as independent arguments. These models have the advantage — over Taylor series expansions — to be well suited for regional and for global models. First results using one week of regional (European) and global data (entire IGS network) from the CODE Analysis Center seem to indicate that under normal ionospheric conditions the ionosphere models are very useful for single-frequency GPS users, i. e. ionospheric refraction effects are greatly reduced if these TEC models are taken into account. Center for Orbit Determination in Europe Paper presented at the 1995 IGS Workshop, Potsdam, Germany, May 15–17, 1995 INTRODUCTION Ionospheric refraction was considered as an important aspect within the GPS group of the Astronomical Institute of the University of Berne (AIUB) for a long time. In the time period when usually only single-band (L1) receivers were available it was important to get insight into the biases introduced in a GPS network by unmodeled ionospheric refraction (Beutler et al., 1988). Later on, it became obvious that short period variations in ionospheric refraction could harm GPS analyses even if dual-band receivers were available (Beutler et al., 1989). In the latter paper there were also clues that valuable information about the ionosphere could be extracted from dual-band GPS data. Modeling and monitoring the ionosphere was the main topic of the Ph.D. thesis (Wild, 1994). In this thesis it could be shown that local ionosphere models like those presented by (Georgiadiou and Kleusberg, 1988) are very efficient to remove — or greatly reduce — the scale bias for single-band receivers operating in the vicinity of dual-band receivers, the data of which were used to establish a local ionosphere model. (Wild, 1994) computed such local ionosphere models for a number of IGS sites over an extended time period. He also describes a procedure to assess the stochastic behaviour of the ionosphere in the vicinity of a GPS station. The principal conclusion was that essential information concerning the ionosphere might be extracted from the IGS network. Local ionosphere models have proved their usefulness on many occasions. However, the concept of having as many ionosphere models as stations in a network like that of the IGS is hardly operational. The modeling techniques used by (Wild, 1994) had to be modified in one important respect before it became possible to replace N local models by one regional or global model based on the data of N stations. Let us briefly review the modeling features as used by (Wild, 1994) and as used below. Wild uses the so-called single-layer model where it is assumed that all free electrons are concentrated in a shell of infinitesimal thickness. This thin shell is located in a height H above a spherical Earth. The height H of this idealized layer is usually set to 350 or 400 kilometers, which corresponds approximately to the peak height of the electron density profile in the F-region of the ionosphere. The electron density E — the surface density of the layer — is assumed to be a function of the geocentric latitude β and the sun-fixed longitude s

Global and regional ionosphere models using the GPS double difference phase observable

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