ABSTRACT:We investigate the extent to which the azimuthally-averaged fields from a three-dimensional, nonhydrostatic, tropical cyclone model can be captured by axisymmetric balance theory. The secondary (overturning) circulation and balanced tendency for the primary circulation are obtained by solving a general form of the Sawyer-Eliassen equation with the diabatic heating, eddy heat fluxes and tangential momentum sources (eddy momentum fluxes, boundary-layer friction and subgrid-scale diffusion) diagnosed from the model. The occurrence of regions of weak symmetric instability at low levels and in the upper-tropospheric outflow layer requires a regularization procedure so that the Sawyer-Eliassen equation remains elliptic. The balanced calculations presented capture a major fraction of the azimuthally-averaged secondary circulation of the three-dimensional simulation except in the boundary layer, where the balanced assumption breaks down and where there is an inward agradient force. In particular, the balance theory is shown to significantly underestimate the low-level radial inflow and therefore the maximum azimuthal-mean tangential wind tendency. In the balance theory, the diabatic forcing associated with the eyewall convection accounts for a large fraction of the secondary circulation. The findings herein underscore both the utility of axisymmetric balance theory and also its limitations in describing the axisymmetric intensification physics of a tropical cyclone vortex.

A new version of the Global Ensemble Forecast System (GEFS, v11) is tested and compared with the operational version (v10) in a 2-yr parallel run. The breeding-based scheme with ensemble transformation and rescaling (ETR) used in the operational GEFS is replaced by the ensemble Kalman filter (EnKF) to generate initial ensemble perturbations. The global medium-range forecast model and the Global Forecast System (GFS) analysis used as the initial conditions are upgraded to the GFS 2015 implementation version. The horizontal resolution of GEFS increases from Eulerian T254 (~52 km) for the first 8 days of the forecast and T190 (~70 km) for the second 8 days to semi-Lagrangian T574 (~34 km) and T382 (~52 km), respectively. The sigma pressure hybrid vertical layers increase from 42 to 64 levels. The verification of geopotential height, temperature, and wind fields at selected levels shows that the new GEFS significantly outperforms the operational GEFS up to days 8–10 except for an increased warm bias over land in the extratropics. It is also found that the parallel system has better reliability in the short-range probability forecasts of precipitation during warm seasons, but no clear improvement in cold seasons. There is a significant degradation of TC track forecasts at days 6–7 during the 2012–14 TC seasons over the Atlantic and eastern Pacific. This degradation is most likely a sampling issue from a low number of TCs during these three TC seasons. The results for an extended verification period (2011–14) and the recent two hurricane seasons (2015 and 2016) are generally positive. The new GEFS became operational at NCEP on 2 December 2015.

While many studies have reported that drought events have substantial negative legacy effects on forest growth, it remains unclear whether wetness events conversely have positive growth legacy effects. Here, we report pervasive and substantial growth enhancement after extreme wetness by examining tree radial growth at 1929 forest sites, satellite-derived vegetation greenness, and land surface model simulations. Enhanced growth after extreme wetness lasts for 1 to 5 years and compensates for 93 ± 8% of the growth deficit after extreme drought across global water-limited regions. Remarkable wetness-enhanced growths are observed in dry forests and gymnosperms, whereas the enhanced growths after extreme wetness are much smaller in wet forests and angiosperms. Limited or no enhanced growths are simulated by the land surface models after extreme wetness. These findings provide new evidence for improving climate-vegetation models to include the legacy effects of both drought and wet climate extremes.

Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of data-driven method (artificial neural networks in this paper) and knowledge-based method (traditional hydrological model) may booster simulation accuracy. In this study, we proposed a new back-propagation (BP) neural network algorithm and applied it in the semi-distributed Xinanjiang (XAJ) model. The improved hydrological model is capable of updating the flow forecasting error without losing the leading time. The proposed method was tested in a real case study for both single period corrections and real-time corrections. The results reveal that the proposed method could significantly increase the accuracy of flood forecasting and indicate that the global correction effect is superior to the second-order autoregressive correction method in real-time correction.

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The Global Ensemble Forecast System (GEFS) is upgraded to version 12, in which the legacy Global Spectral Model (GSM) is replaced by a model with a new dynamical core - the Finite Volume Cubed-Sphere Dynamical Core (FV3). Extensive tests were performed to determine the optimal model and ensemble configuration. The new GEFS has cubed-sphere grids with a horizontal resolution of about 25-km and an increased ensemble size from 20 to 30. It extends the forecast length from 16 days to 35 days to support subseasonal forecasts. The stochastic total tendency perturbation (STTP) scheme is replaced by two model uncertainty schemes: the Stochastically Perturbed Physics Tendencies (SPPT) scheme and Stochastic Kinetic Energy Backscatter (SKEB) scheme. Forecast verification is performed on a period of more than two years of retrospective runs. The results show that the upgraded GEFS outperforms the operational-at-the-time version by all measures included in the GEFS verification package. The new system has a better ensemble error-spread relationship, significantly improved skills in large-scale environment forecasts, precipitation probability forecasts over CONUS, tropical cyclone track and intensity forecasts, and significantly reduced 2-m temperature biases over Northern America. GEFSv12 was implemented on September 23, 2020.

The stable isotopes of oxygen and hydrogen in the water cycle have become a significant tool to study run-off formation, hydrograph separation, and the origin of precipitation. Precipitation assessment based on isotopic data has a potential implication for moisture sources. In the study, δD and δ18O of precipitation samples collected from six rainfall events were analyzed for stable isotope composition to provide implication of isotopic characteristics as well as moisture sources in Hemuqiao basin within Lake Tai drainage basin, eastern China. In these events, stable oxygen and hydrogen isotopic composition of precipitation had strong variations. Models of the meteoric water line and deuterium excess for different rainfall types (typhoon and plum rain, which is caused by precipitation along a persistent stationary front known as the Meiyu front for nearly two months during the late spring and early summer between eastern Russia, China, Taiwan, Korea and Japan) were established. Compared with plum rain, the moisture source of typhoon events had higher relative humidity and temperature. Moisture transport pathways were traced using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT Model, developed by NOAA, Washington DC, U.S.) to verify the linkage with isotopic composition and moisture source. The moisture sources of typhoon events mostly derived from tropical ocean air with higher isotopic value, while that of plum rain events came from near-source local air with lower isotopic value.

The summer precipitation from June to September in the source region of the Yellow River accounts for about 70% of the annual total, and its decrease would cause further water shortage problems. Consequently, the objectives of this study are to improve the understanding of the linkages between the precipitation in the source region of the Yellow River and global teleconnection patterns, and to predict the summer precipitation based on revealed teleconnections. Spatial variability of precipitation was investigated based on three homogeneous sub-regions. Principal component analysis and singular value decomposition were used to find significant relationships between the precipitation and global teleconnection patterns using climate indices. A back-propagation neural network was developed to predict the summer precipitation using significantly correlated climate indices. It was found that precipitation in the study area is positively related to North Atlantic Oscillation, West Pacific Pattern and El Niño Southern Oscillation, and inversely related to Polar Eurasian pattern. Summer precipitation was overall well predicted. The Pearson correlation coefficient between predicted and observed summer precipitation was, in general, larger than 0.6. The results can be used to predict the summer precipitation and to improve integrated water resources management in the Yellow River basin.