Zhizhao Liu’s research while affiliated with The Hong Kong Polytechnic University and other places

Integrated water vapor (IWV) is a dominant influence element in radiation absorption, energy transfer, and water circulation on both local and global scales. The Sentinel-3 Ocean and Land Color Imager (OLCI) instrument provides operational IWV measurements using a 2-band ratio of an IWV absorption channel (O19; 900 nm) and a referenced channel (O18; 885 nm). However, the operational OLCI/Sentinel-3 satellite product does not offer IWV estimates under cloudy sky conditions, as OLCI-sensed near-infrared data have considerable uncertainties when clouds are in existence. We develop a practical machine learning-based retrieval algorithm to derive IWV estimates from OLCI near-infrared radiance observations under all sky conditions. The retrieval method utilizes O19 900-nm and O20 940-nm IWV absorption bands as well as O18 885-nm and O21 1,020-nm referenced bands, based on both 2-band and 3-band ratio methods. IWV from Global Navigation Satellite System (GNSS) are used as the desired IWV retrievals. The results show that all newly derived IWV retrievals have an excellent agreement with reference IWV from additional GNSS and radiosonde data, regardless of sky weather conditions. The weighted mean IWV retrievals present the highest performance with GNSS and radiosonde IWV (correlation coefficient: 0.86 and 0.85; root-mean-square error: 2.85 and 3.49 mm; mean bias: -0.14 and -0.99 mm). The newly retrieved cloudy-sky IWV is comparable to operational clear-sky IWV, denoting the capability and effectiveness of the retrieval algorithm. The retrieval approach exhibits a dependable performance in both spatial and temporal dimensions, which could be employed in other areas and periods.

Aviation communication is significant for the safe, efficient, and orderly operation of air traffic. The aviation industry relies on a sophisticated network to maintain air‐ground communications. However, space weather events can disrupt the ionosphere conditions and damage satellites, leading to High‐Frequency (HF) communication blackouts and satellite communication failures. These disruptions can jeopardize flight safety, especially for flights over polar regions. In response, strategies such as cancellations, rescheduling, or rerouting to lower latitudes may be necessary, despite the low flight efficiency and substantial financial losses. With the background of the anticipated solar maximum in 2025 and a growing number of polar flights, it is indispensable to have a comprehensive understanding of the space weather effects on aviation communication and develop constructive strategies from an Air Traffic Management (ATM) perspective. Hence, we simulate scenarios with different durations of communication failures and assess the corresponding economic losses. Based on the data derived from historical polar flights in 2019, there are daily 18 polar flights with trajectories crossing the north polar region higher than 82°N. Simulation results show that the economic losses associated with these polar flights can range from €0.03 million to €1.32 million, depending on both the duration of communication failures and the adopted air traffic management strategies. We believe that this study can shed light on the effects of space weather‐induced communication failures on polar flight operations and provide guidance for mitigating these effects in the aviation industry.

Plain Language Summary Integrated water vapor (IWV) is the largest natural greenhouse component, which plays a crucially important role in weather, climate, and other related fields. Remote sensing of IWV from satellite‐based instruments provides a unique technique for monitoring atmospheric water vapor distribution at proper spatial and temporal resolutions in both local and global areas. However, non‐geostationary satellite‐retrieved IWV observations have much lower temporal resolutions compared to geostationary satellite‐sensed IWV measurements. The previously published improvements in the temporal resolution of non‐geostationary satellite‐retrieved IWV estimates are primarily performed based on data fusion approaches using reanalysis‐based high‐temporal‐resolution IWV data. We propose a feasible IWV retrieval algorithm for directly retrieving high‐temporal‐resolution IWV data from non‐geostationary Ocean and Land Color Instrument (OLCI)‐sensed near‐infrared radiance observations. For the first time, this study provides implications for the direct retrieval of high‐temporal‐resolution IWV estimates from non‐geostationary satellite measurements. The retrieval algorithm has significant potential to be applicable to other non‐geostationary OLCI‐like instruments, such as Medium Resolution Imaging Spectrometer (MERIS), Medium Resolution Spectral Imager (MERSI), and Moderate Resolution Imaging Spectroradiometer (MODIS).

The Global Precipitation Measurement (GPM) Microwave Imager (GMI) is a microwave (MW) radiometer that has near-global coverage and frequent revisit time. To date, operational total column water vapor (TCWV) data records from the GPM GMI sensor have been exclusively offered over oceanic regions. It is challenging to retrieve TCWV over land from satellite MW measurements because of varying land surface characteristics. In this paper, a novel Light Gradient Boosting Machine-based retrieval algorithm is proposed to derive TCWV over land from GMI-sensed MW brightness temperature (BT) observations. The GMI-observed MW BT at 18.7 GHz and 23.8 GHz, differential BT between 18.7 GHz and 23.8 GHz, latitude, longitude, and month are selected and utilized as the input variables of the retrieval approach, because of their strong correlation with satellite-sensed MW TCWV retrievals. Instead of surface emissivity data or radiative transfer model, we take into account the spatial and temporal elements, namely latitude, longitude, and month. The training of the retrieval method is performed based on ground-based TCWV estimates from worldwide 4,471 Global Navigation Satellite System (GNSS) stations in 2017. The performance of the newly proposed retrieval algorithm is independently validated in a worldwide coverage using reference TCWV from additional 4,341 GNSS stations in 2018–2020 and 605 radiosonde stations in 2017–2020. The newly retrieved TCWV estimates over land have a correlation coefficient of 0.76 and 0.83, a root-mean-square error (RMSE) of 5.82 mm and 6.02 mm, a relative RMSE of 34.91% and 34.36%, and a mean bias of 0.02 mm and −0.42 mm compared to reference TCWV from GNSS and radiosonde, respectively. The performance of the retrieval algorithm is satisfactory when compared to that of land-purpose TCWV of other satellite missions, though we have not used either surface emissivity data or radiative transfer model. This result increases confidence in retrieving TCWV over land from satellite-sensed MW BT measurements based on machine learning using ground-based TCWV observations. The newly developed retrieval algorithm has the potential for integration into operational satellite missions or meteorological services, thereby enhancing weather forecasting, climate modeling, and other relevant applications.

The ionosphere exhibits complex variations due to the influences from above and below. To distinguish the source of ionospheric disturbances is important for understanding the variation process and the coupling mechanism among different regions. Using the ionospheric total electron content (TEC) derived from Global Navigation Satellite System observations, the ionospheric disturbances during the super typhoon Hato in 2017 that was accompanied by a weak geomagnetic storm are revisited, including the ionospheric deviation and traveling ionospheric disturbances (TIDs). It is found that the ionospheric TEC in the low‐latitude region of China experienced a significant enhancement (200% compared to the quiet geomagnetic day) on Hato landing day. This enhancement covers the northern and southern equatorial ionization anomaly (EIA) region from 80°E to 180°E. Considering the geomagnetic condition, the hmF2 and the O/N2 ratio in thermosphere, it is concluded that this enhancement is not related to the typhoon, but to the coinciding weak geomagnetic storm. Additionally, several medium‐scale TIDs are verified from differential TEC data in China low latitude region during Hato period. Most of them occur after sunset and their propagating direction is southwest that often occur in East‐Asian sector in summer months, which are not related to the typhoon. While a few TIDs with concentric wavefront (Concentric TIDs) are also observed on the day before Hato landfall that should be excited in the deep convective region of the typhoon. Because the ionosphere is affected by disturbances both from above and below, it should be careful to determine the source of the ionospheric disturbances.

Atmospheric wet delay caused by Precipitable Water Vapor (PWV) significantly impacts the performance of many geodetic surveying systems such as Global Navigation Satellite System (GNSS). In this study, we use wet delay corrections forecast by the Weather Research and Forecasting (WRF) model to enhance GNSS Precise Point Positioning (PPP) during two observation periods with two different weather conditions, that is, period 1: March 01 to 14, 2020 (average PWV: 23.5 kg/m²) and period 2: June 02 to 15, 2020 (flooding weather with average PWV: 55.6 kg/m²), over the South China. PWV data from 277 to 263 GNSS stations are assimilated into WRF model to enhance the WRF water vapor forecasting capability for period 1 and period 2, respectively. Wet delay corrections from two different WRF configurations, that is, WRF no data assimilation and WRF with assimilation of GNSS PWV, are used to augment the PPP. Totally, eight WRF‐enhanced PPP schemes are tested. The results show that WRF‐enhanced PPP schemes generally have a better positioning performance in the up component than traditional PPP. After using WRF wet delay corrections, for static mode, the vertical positioning accuracy is improved by 14.6% and 33.7% for period 1 and period 2, respectively. The corresponding convergence time are reduced by 41.8% and 25.0% for period 1 and period 2, respectively. For kinematic mode, the positioning accuracy improvements in the up component reach 13.8% and 19.0% for period 1 and period 2, respectively. The kinematic PPP convergence time is reduced by up to 8.2% for period 1.

The precise point positioning real-time kinematic (PPP-RTK) is a high-precision global navigation satellite system (GNSS) positioning technique that combines the advantages of wide-area coverage in precise point positioning (PPP) and of rapid convergence in real-time kinematic (RTK). However, the PPP-RTK convergence is still limited by the precision of slant ionospheric delays and tropospheric zenith wet delay (ZWD), which affects the PPP-RTK network parameters estimation and user positioning performance. The present study aims to construct a PPP-RTK model augmented with a priori ZWD values derived from the global forecast system (GFS) product (a global numerical weather prediction (NWP) model) to improve the PPP-RTK performance. This study gives a priori ZWD values and conversion based on the GFS products, and the full-rank GFS-augmented undifferenced and uncombined (UDUC) PPP-RTK network model is derived. To verify the performance of GFS-augmented UDUC PPP-RTK, a comprehensive evaluation using 10-day GNSS observation data from three different GNSS station networks in the United States (US), Australia, and Europe is conducted. The results show that with the GFS ZWD a priori information, PPP-RTK performance significantly improves at the initial filtering stage, but this advantage gradually decays over time. Based on 10-day positioning results for all user stations, the GFS ZWD-augmented PPP-RTK approach reduces the average convergence time by 46% from 10.0 to 5.4 min, the three-dimensional root-mean-square (3D-RMS) error by 5.7% from 3.5 to 3.3 cm, and the time to first fix (TTFF) value by 35.8% from 6.7 to 4.3 min, all when compared to the traditional PPP-RTK without GFS ZWD constraints.

The knowledge of atmospheric water vapor distribution is vital to our understanding of weather and climate. In this article, we propose a new calibration scheme based on machine learning to enhance the observational performance of official all-weather precipitable water vapor (PWV) data records from thermal infrared (IR) measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The calibration scheme takes several influence factors into consideration, which are linked with the performance of satellite-retrieved IR PWV measurements. The ground-based water vapor data, acquired from 214 Global Positioning System (GPS) sites across China in 2016, are regarded as reference PWV to train the machine learning based calibration approaches. The evaluation result during 2017-2019 across China shows that the calibrated MODIS IR all-weather PWV product agrees better with GPS-retrieved reference PWV observations, with R ² of 0.88-0.94, root-mean-square-error (RMSE) of 2.79-4.08 mm, and mean bias of 0.16-0.52 mm. The RMSE between water vapor measurements from MODIS and GPS can be reduced by 41.74%, 45.76%, 44.29%, and 49.04% in confident-clear, probably-clear, probably-cloudy, and confident-cloudy conditions, respectively. Our methods, developed based on the new calibration scheme, could be a promising tool to the calibration of other satellite-derived IR all-weather water vapor products, which could be also extended to other regions or time periods.

Precipitable water vapor (PWV) data from satellite-sensed near-infrared (NIR) measurements offer a unique source for monitoring atmospheric water vapor distribution locally and globally. However, the observational quality of satellite-based operational NIR PWV products is considerably affected by the presence of clouds. We develop a spatial and temporal cloud fraction based calibration method (STCFCM) to calibrate satellite-sensed NIR PWV products and improve the PWV accuracy. The STCFCM is built based on Light Gradient Boosting Machine using cloud fraction, together with spatial-temporal fields – latitude, longitude, height, and month. The newly calibrated PWV estimates from the MODIS sensor onboard the Terra satellite show that the STCFCM-estimated PWV data exhibit a better agreement with reference PWV estimates from GNSS and radiosonde observations. The root-mean-square error of MODIS/Terra operational PWV products is reduced by 55.53% from 11.40 mm to 5.07 mm compared to GNSS PWV and 60.74% from 14.67 mm to 5.76 mm compared to radiosonde PWV. The calibrated all-weather PWV estimates outperform operational clear-sky PWV products, highlighting the effectiveness of the STCFCM in calibrating satellite-sensed NIR PWV retrievals, particularly for cloudy sky conditions. The newly developed STCFCM approach is the first one in the research community to calibrate global PWV data from satellite NIR measurements. It is a promising technique to calibrate remote sensing PWV products from other satellite observations under all weather conditions.