After decades of research and development, the WSR-88D (NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data (PRD) that have the potential to improve weather observations, quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction (NWP) models. In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.
This study examines associations between California Central Valley (CCV) heat waves and the Madden Julian Oscillation (MJO). These heat waves have major economic impact. Our prior work showed that CCV heat waves are frequently preceded by convection over the tropical Indian and eastern Pacific oceans, in patterns identifiable with MJO phases. The main analysis method is lagged composites (formed after each MJO phase pair) of CCV synoptic station temperature, outgoing longwave radiation (OLR), and velocity potential (VP). Over the CCV, positive temperature anomalies occur only after the Indian Ocean (phases 2-3) or eastern Pacific Ocean (phases 8-1) convection (implied by OLR and VP fields). The largest fractions of CCV hot days occur in the two weeks after onset of those two phase pairs. OLR and VP composites have significant subsidence and convergence above divergence over the CCV during heat waves, and these structures are each part of larger patterns having significant areas over the Indian and Pacific Oceans. Prior studies showed that CCV heat waves can be roughly grouped into two clusters: Cluster 2 is preceded by a heat wave over northwestern North America, while Cluster 1 is not. OLR and VP composite analyses are applied separately to these two clusters. However, for Cluster 2, the subsidence and VP over the CCV are not significant, and the large-scale VP pattern has low correlation with the MJO lagged composite field. Therefore, the association between the MJO convection and subsequent CCV heat wave is more evident in Cluster 1 than Cluster 2.
The CarbonTracker (CT) model has been used in previous studies for understanding and predicting the sources, sinks, and dynamics that govern the distribution of atmospheric CO2 at varying ranges of spatial and temporal scales. However, there are still challenges for reproducing accurate model-simulated CO2 concentrations close to the surface, typically associated with high spatial heterogeneity and land cover. In the present study, we evaluated the performance of nested-grid CT model simulations of CO2 based on the CT2016 version through comparison with in-situ observations over East Asia covering the period 2009-13. We selected sites located in coastal, remote, inland, and mountain areas. The results are presented at diurnal and seasonal time periods. At target stations, model agreement with in-situ observations was varied in capturing the diurnal cycle. Overall, biases were less than 6.3 ppm on an all-hourly mean basis, and this was further reduced to a maximum of 4.6 ppm when considering only the daytime. For instance, at Anmyeondo, a small bias was obtained in winter, on the order of 0.2 ppm. The model revealed a diurnal amplitude of CO2 that was nearly flat in winter at Gosan and Anmyeondo stations, while slightly overestimated in the summertime. The model's performance in reproducing the diurnal cycle remains a challenge and requires improvement. The model showed better agreement with the observations in capturing the seasonal variations of CO2 during daytime at most sites, with a correlation coefficient ranging from 0.70 to 0.99. Also, model biases were within -0.3 and 1.3 ppm, except for inland stations (7.7 ppm).
The East Asian westerly jet (EAJ), an important midlatitude circulation of the East Asian summer monsoon system, plays a crucial role in affecting summer rainfall over East Asia. The multimodel ensemble of current coupled models can generally capture the intensity and location of the climatological summer EAJ. However, individual models still exhibit large discrepancies. This study investigates the intermodel diversity in the longitudinal location of the simulated summer EAJ climatology in the present-day climate and its implications for rainfall over East Asia based on 20 CMIP5 models. The results show that the zonal location of the simulated EAJ core is located over either the midlatitude Asian continent or the western North Pacific (WNP) in different models. The zonal shift of the EAJ core depicts a major intermodel diversity of the simulated EAJ climatology. The westward retreat of the EAJ core is related to a warmer mid-upper tropospheric temperature in the midlatitudes, with a southwest-northeast tilt extending from Southwest Asia to Northeast Asia and the northern North Pacific, induced partially by the simulated stronger rainfall climatology over South Asia. The zonal shift of the EAJ core has some implications for the summer rainfall climatology, with stronger rainfall over the East Asian continent and weaker rainfall over the subtropical WNP in relation to the westward-located EAJ core.
In the South China Sea, sea fog brings severe disasters every year, but forecasters have yet to implement an effective sea-fog forecast. To address this issue, we test a liquid-water-content-only (LWC-only) operational sea-fog prediction method based on a regional mesoscale numerical model with a horizontal resolution of about 3 km, the Global and Regional Assimilation and Prediction System (GRAPES), hereafter GRAPES-3km. GRAPES-3km models the LWC over the sea, from which we infer the visibility that is then used to identify fog. We test the GRAPES-3km here against measurements in 2016 and 2017 from coastal-station observations, as well as from buoy data, data from the Integrated Observation Platform for Marine Meteorology, and retrieved fog and cloud patterns from Himawari-8 satellite data. For two cases that we examine in detail, the forecast region of sea fog overlaps well with the multi-observational data within 72 h. Considering forecasting for 0-24 h, GRAPES-3km has a 2-year-average equitable threat score (ETS) of 0.20 and a Heidke skill score (HSS) of 0.335, which is about 5.6% (ETS) and 6.4% (HSS) better than our previous method (GRAPES-MOS). Moreover, the stations near the particularly foggy region around the Leizhou Peninsula have relatively high forecast scores compared to other sea areas. Overall, the results show that GRAPES-3km can roughly predict the formation, evolution, and dissipation of sea fog on the southern China coast.