Forecasting Weather and Renewable Energy with the NOAA's High-Resolution WRF Model

Renewable energy is frequently cited as the most important shift the world can make to stave off the worst consequences of global warming. This is due to the fact that renewable energy sources like solar and wind don't produce greenhouse gases like carbon dioxide, which contributes to global warming. Accurate weather forecasts can help to significantly increase the efficiency of operation of these fossil fuel plants as well as the entire electrical grid system, resulting in lower costs as well as lower CO2 emissions. Accurate weather forecasts can also advance predict when wind power will ramp up or down. The growth of wind energy as a developing part of the country's energy portfolio will be accelerated by lowering the costs of integrating wind energy onto the grid. In this work, we focus on improving weather forecast, particular wind speed, and wind power production for the electricity trading financial market, with the application of high-resolution WRF model. 

Related Publications

 Zhang, W., & Zoltan, T. (2022-2023). Improving forecast of wind and associated power production in the U.S. with a high-resolution WRF model. In preparation. 

Increasing Impacts of Heatwaves and Extreme Precipitation in Eastern China

In 2022, China issued the first national red alert for extreme drought and heatwaves, with the maximum 2-m air temperature above 40 °C (104 °F) over a period of 48 hours or more in eastern China. As a matter of fact, it has been reported that there is an increased frequency of heatwaves and extreme precipitation in an anthropogenically warmed climate, based on observational data and climate modeling. However, the accurate link between extreme precipitation events (EPEs) and heatwaves (HWs), and changes in these extremes and associated socio-economic impacts in eastern China have not been fully resolved. Mainly, this research focused on understanding heatwaves and extreme precipitation events with CMIP6 model projections. 
Heatwave affects global regions in 2022 (source: NASA) 

Related Publications

Yao, Y., Zhang, W., & Kirtman, B. (2022). Increasing impacts of heatwaves and atmospheric rivers in Eastern China. Submitted to Climate Dynamics.  

The Signal-to-noise Paradox in Climate Simulations and Prediction

One emerging topic in climate prediction is the issue of the so-called “signal-to-noise paradox”, characterized by too small signal-to-noise ratio in current model predictions that cannot reproduce the signal in the real world. Recent studies have suggested that seasonal-to-decadal climate can be more predictable than ever expected due to this paradox. However, the mechanism behind the signal-to-noise paradox has yet to be fully understood. This study introduces a Markov model framework to represent the ensemble forecasts and the signal-to-noise paradox. The simulations suggest that the paradox is primarily due to the shorter persistence or overestimated noise variance in models than the observational estimates. The Markov model framework is applied to determine the existence of the paradox in CMIP5 and CMIP6 models, with respect to the NAO index and surface climate, including sea level pressure, precipitation, and sea surface temperature. The results suggest that the signal-to-noise paradox is widespread in current global climate models but can potentially be ameliorated with high-resolution ocean models.



(a) Difference of decadal SST predictability between observations and CMIP5 historical simulations. (b) Chance of existence for the signal-to-noise paradox based on 30 CMIP5 historical simulations. The Markov Model framework is introduced to estimate the existence of the signal-to-noise paradox. 

Related Publications

Zhang, W., & Kirtman, B. (2019). Understanding the signal‐to‐noise paradox with a simple Markov model. Geophysical Research Letters.

Zhang, W., Kirtman, B. Siqueira, L. Clement, A., Xia, J (2021). Understanding the signal-to-noise paradox in decadal climate predictability from CMIP5 and an eddying global coupled model. Climate Dynamics.

Zhang, W., Xiang, B., Kirtman, B., Jia L., He, J., Delworth, T., and others. The signal-to-noise paradox in the tropical Pacific. Prepare to submit to Nature Climate Change

Improving Sub-seasonal Climate Prediction based on GFDL's Next Generation Seamless Modeling Forecast System - SPEAR 

There is increasing interest in extreme weather and climate events, which have led to devastating socioeconomic losses in recent years. Understanding the sources of subseasonal-to-seasonal (S2S) predictability and associated impacts may potentially improve the prediction skills of extreme events and provide early warning systems for the community. This project aims to examine subseasonal (3-6 weeks) climate prediction based on the GFDL's next general modeling system - Seamless System for Prediction and EArth System Research (SPEAR). Based on a 10-member 20-year hindcast experiment using SPEAR model, we intend to assess the prediction skills of atmospheric rivers and wintertime cold extremes over subseasonal timescales. 



Week-4 (lead time: 21-27 days) Prediction skill of atmospheric rivers based on SPEAR model hindcast. (left) winter season - DJF (right) summer season - JJA 

Related Publications

Zhang, W., Xiang, B., Tseng, K., Johnson, N., Delworth, T., and others. Assessment of atmospheric river subseasonal prediction skill in GFDL SPEAR model hindcast. In preparation.

Zhang, W., Xiang, B., Tseng, K., Johnson, N., Delworth, T., and others. Subseasonal Prediction of Cold Extremes in boreal winter based on GFDL SPEAR model. In preparation.

Ocean eddies and Climate Variability & Predictability

This project seeks to understand how ocean eddies impact large-scale climate variability and predictability. This requires global climate simulations conducted at resolutions (eddy-resolved, ocean model resolution 0.1x0.1) that have never before been attempted. The figures below show a snapshot of the surface current speeds in this high-resolution simulation compared to the typical resolutions used. Capturing the details of these currents has been shown to dramatically affect the global distribution of rainfall. 



Snapshot of monthly mean temperature (°C) at 5 m (model level 1) for the North Atlantic region: (a) LR global climate model, (b) HR global climate model, and (c) observations from Jet Propulsion Lab Multiscale Ultra‐high Resolution Sea Surface Temperature.

Related Publications

Zhang, W., Kirtman, B. Siqueira, L., Xiang, B., Infanti J., Perlin, N. The Influence of a resolved Gulf Stream on the decadal variability of southeast US rainfall. Geophysical Research Letters. In revision. 

AI for Climate Prediction (Multi-year ENSO Prediction)

The El Niño-Southern Oscillation (ENSO) significantly influences Earth’s climate, ecosystems, and human societies; Yet, forecasting ENSO at lead times of more than a year remains a challenge for the whole climate community. This project aims at developing a deep learning system by training a convolutional neural network to improve multi-year ENSO prediction, which will potentially supplement current dynamical forecast systems based on global climate models.
A simple Architecture of the deep learning ENSO prediction system. The black box in the input maps shows the ocean region used to calculate the Niño3.4 index. 

Funding: Microsoft AI for Earth Azure Grant

Related Publications

Zhang, W., Geng J., Xia, J., Kirtman, B. Improving multi-year ENSO prediction with an optimized convolutional neural network. In preparation for Journal of Climate. 

Decadal Climate Predictability from an Interactive Ensemble Coupled Model 

Decadal climate predictability has received considerable scientific interest in recent years, yet the limits and mechanisms for decadal predictability are currently not well known. It is widely accepted that noise due to internal atmospheric dynamics at the air-sea interface influences predictability. The purpose of this paper is to use the interactive ensemble (IE) coupling strategy to quantify how internal atmospheric noise at the air-sea interface impacts decadal predictability. The IE technique can significantly reduce internal atmospheric noise and has proven useful in assessing seasonal-to-interannual variability and predictability. Here we focus on decadal timescales and apply the nonlinear local Lyapunov exponent method to the Community Climate System Model comparing control simulations with IE simulations. This is the first time the nonlinear local Lyapunov exponent has been applied to the state-of-the-art coupled models. The global patterns of decadal predictability are discussed from the perspective of internal atmospheric noise and ocean dynamics.


Spatial distributions of decadal SST predictability using the NLLE method and decadal timescale ratio: (a, b) ERSST.v3b, (c, d) COBE-SST2, (e, f) CCSM4 CTRL and IE (Interactive Ensemble) Simulations, (g, h) differences between IE and CTRL simulations. 
Funding: DOE. Grant Number: DE-SC0019433; NSF. Grant Numbers: OCE1559151, OCE1419569, AGS1558837

Related Publications

Zhang, W., Kirtman, B.P. Estimates of decadal climate predictability from an interactive ensemble coupled model. Geophysical Research Letters. DOI: 10.1029/2018GL081307​

Climate Extremes and Humidity Index in China 

Recently, climate extremes have become one of the most significant and attractive themes in climate science, and global warming has been shown to trigger and exacerbate intense precipitation events, contributing to disastrous damages to natural and human systems. The main purpose of our research is to investigate the change of extreme drought/wet events during the past several decades in the context of global warming. 
Left: Study area - Huaihe River Basin, Eastern China. Middle: interanual variations of extreme events in the Huaihe River Bason from 1960 to 2013. Right: Morlet wavelet analysis of extreme wet events in the study area. 

Related Publications

Zhang, W., Pan, S., Cao, L., Cai, X., Zhang, K., Xu, Y., & Xu, W. (2015). Changes in extreme climate events in eastern China during 1960–2013: a case study of the Huaihe River Basin. Quaternary International, 380, 22-34. DOI: 10.1016/j.quaint.2014.12.038 [pdf]
Zhang, K., Pan, S., Cao, L., Wang, Y., Zhao, Y., & Zhang, W. (2014). Spatial distribution and temporal trends in precipitation extremes over the Hengduan Mountains region, China, from 1961 to 2012. Quaternary International, 349, 346-356. DOI: 10.1016/j.quaint.2014.04.050 [pdf]

Cesium-137 Atmospheric Fallout (Model/Software Development)

Soil erosion is a serious environmental problem closely associated with sustainable development and ultimately the survival of mankind. Cs-137, a unique artificial radioactive tracer, has been widely applied to the study of soil erosion and deposition since the 1960s. One of the prerequisites of soil erosion study is to estimate Cs-137 reference inventory (CRI). The creative part of my research is that I built a Modified CRI Model in China, which can help calculate CRI for areas with no Cs-137 atmospheric fallout records. 



Related Publications

Zhang, W., Pan, S., Zhang, K., Cao, L., Zhao, J. (2015). Study of the Cesium-137 Reference Inventory in the Mainland of China. Acta Geographica Sinica, 70(9): 1477-. DOI: 10.11821/dlxb201509010 [pdf]
Xu, W., Pan, S., Jia, P, Yang, X., Cao, L., Zhang, W., Ruan, X., Guan, Y. (2014). 137Cs Reference Inventory and its distribution in surface soil along the Fangchenggang coastal zone of Beibu Gulf. Geographical Research, 34(4): 655-665. DOI: 10.11821/dlyj201504005 [pdf]