Delayed Atmospheric Temperature Response to ENSO SST:Role of High SST and the Western Pacific

Tropical zonally symmetric atmospheric warming occurs during ENSO＇s warm phase, and lags the equatorial east Pacific sea surface temperatures （SSTs） by 3-4 months. The role of the Indian and Atlantic oceans on the atmospheric delayed response has been pointed out by earlier studies. For 1951-2004, a regression analysis based on the observed SST data shows the western Pacific has a similarly important role as the Indian and Atlantic. Nevertheless, there is time mismatch of around 1-2 months between the zonally averaged tropical SST anomalies and the atmospheric temperature anomalies. It is expected that the tropospheric temperature should be controlled by diabatic heating forcing, which is sensitive primarily to SST anomalies over regions of high climatological SST, rather than to the tropical mean SST anomalies. To describe this mechanism, we propose a parameterization scheme of diabatic heating anomalies dependant on SST anomalies and climatological SST. The 1-2 month mismatch between tropical mean SST anomalies and air temperature anomalies is reconciled by the fact that the tropical mean heating anomalies are dominated by the SST anomalies over regions of high climatological SST, and lag the tropical mean SST anomalies by 1 month. The mechanism described by this parameterization scheme joins several physical processes of ENSO with reasonable time intervals. And the parameterized heating anomalies work better than the tropical mean SST anomalies for capturing the atmospheric temperature signal relative to ENSO.

Crowdsourcing RTK:a new GNSS positioning framework for building spatial high-resolution atmospheric maps based on massive vehicle GNSS data

High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution(AR)in precise positioning.However,traditional real-time precise positioning frameworks(i.e.,NRTK and PPP-RTK)depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network.This results in limited public appeal.With the mass production of autonomous driving vehicles,more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps.In this study,we propose a new GNSS positioning framework that relies on dual base stations,massive vehicle GNSS data,and crowdsourced atmospheric delay correction maps(CAM).The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way.Specifically,the map consists of between-station single-differenced ionospheric and tropospheric delays.We introduce the whole framework of CAM initialization for individual vehicles,on-cloud CAM maintenance,and CAM-augmented user-end positioning.The map data are collected and preprocessed in vehicles.Then,the crowdsourced data are uploaded to a cloud server.The massive data from multiple vehicles are merged in the cloud to update the CAM in time.Finally,the CAM will augment the user positioning performance.This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other.We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales.We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.

Retrieval of Vertical Distribution of Tropospheric Refractivity through Ground-Based GPS Observation

In the present reported study, the vertical distributions of local atmospheric refractivity were retrieved from ground- based GPS observations at low elevation angles. An improved optimization method was implemented at altitudes of 0-10 km to search for a best-fit refractivity profile that resulted in atmospheric delays most similar to the delays calculated from the observations. A ray-tracing model was used to simulate neutral atmospheric delays corresponding to a given refractivity profile. We initially performed a ＂theoretical retrieval＂, in which no observation data were involved, to verify the optimization method. A statistical relative error of this ＂theoretical retrieval＂ （-2% to 2%） indicated that such a retrieval is effective. In a practical retrieval, observations were obtained using a dual-frequency GPS receiver, and its initial value was provided by CIRA86aQ_UoG data. The statistical relative errors of the practical retrieval range from -3% to 5% were compared with co-located radiosonde measurements, Results clearly revealed diurnal variations in local refractivity prc,files, The results also suggest that the general vertical distribution of refractivity can be derived with a high temporal resolution. However, further study is needed to describe the vertical refractivity gradient clearly.

Subsidence Velocity Estimation Using PS-Based Time Series DInSAR

Satellite differential synthetic aperture radar interferometry （DInSAR） has two limitations, i. e. , spatio-temporal decorrelation and atmospheric influence, for the application of regional deformation mapping. This paper proposed a new approach called time series DInSAR based on permanent scatters （PS） to overcome the limitations, and the relevant mathematical model and computing method are also developed. With the proposed algorithm, the experiments were performed to estimate the subsidence velocity field over Shanghai using 26 ERS-1/2 SAR images acquired from 1992 to 2002. The deformation signatures, atmospheric delay and other errors were separated effectively, and the subsidence velocity field over Shanghai was also derived.