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Yanyi He
,
Kun Yang
,
Yanghang Ren
,
Mijun Zou
,
Xu Yuan
, and
Wenjun Tang

The 2021 low solar radiation over southeastern Tibetan Plateau was mainly caused by abnormally strong southerlies and was further enhanced by anthropogenic aerosols and GHGs-induced warming, and consequently reduced vegetation growth.

Free access
Vannia Aliaga-Nestares
,
Gustavo De La Cruz
, and
Ken Takahashi

Abstract

Multiple linear regression models were developed for 1-3-day lead forecasts of maximum and minimum temperature for two locations in the city of Lima, in the central coast of Peru (12°S), and contrasted with the operational forecasts issued by the National Meteorological and Hydrological Service - SENAMHI and the output of a regional numerical atmospheric model. We developed empirical models, fitted to data from the 2000-2013 period, and verified their skill for the 2014-2019 period. Since El Niño produces a strong low-frequency signal, the models focus on the high-frequency weather and subseasonal variability (60-day cutoff).

The empirical models outperformed the operational forecasts and the numerical model. For instance, the high-frequency annual correlation coefficient and root mean square error (RMSE) for the 1-day lead forecasts were 0.37-0.53 and 0.74-1.76°C for the empirical model, respectively, but around −0.05-0.24 and 0.88-4.21°C in the operational case. Only three predictors were considered for the models, including persistence and large-scale atmospheric indices. Contrary to our belief, the model skill was lowest for the austral winter (June-August), when the extratropical influence is largest, suggesting an enhanced role of local effects. Including local specific humidity as a predictor for minimum temperature at the inland location substantially increased the skill and reduced its seasonality.

There were cases in which both the operational and empirical forecast had similar strong errors and we suggest mesoscale circulations, such as the Low-Level Cyclonic Vortex over the ocean, as the culprit. Incorporating such information could be valuable for improving the forecasts.

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Vishal Batchu
,
Grey Nearing
, and
Varun Gulshan

Abstract

We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm of soil. Input predictors include Sentinel-1 (active radar), and Sentinel-2 (multispectral imagery) as well as geophysical variables from SoilGrids and modelled soil moisture fields from SMAP-USDA and GLDAS. The model was trained and evaluated on data from ~1000 in-situ sensors globally over the period 2015 - 2021 and obtained an average per-sensor correlation of 0.707 and ubRMSE of 0.055 m3/m3, and can be used to produce a soil moisture map at a nominal 320m resolution. These results are benchmarked against 14 other soil moisture evaluation research works at different locations, and an ablation study was used to identify important predictors.

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Ali Tokay
,
Charles N. Helms
,
Kwonil Kim
,
Patrick N. Gatlin
, and
David B. Wolff

Abstract

Improving estimation of snow water equivalent rate (SWER) from radar reflectivity (Ze), known as a SWER(Ze) relationship, is a priority for NASA’s Global Precipitation Measurement (GPM) mission ground validation program as it is needed to comprehensively validate spaceborne precipitation retrievals. This study investigates the performance of eight operational and four research based SWER(Ze) relationships utilizing Precipitation Imaging Probe (PIP) observations from the International Collaborative Experiment – Pyeongchang Olympics and Paraolympics (ICE-POP 2018) field campaign. During ICE-POP 2018, there were 10 snow events that are classified by synoptic conditions as either cold low or warm low and a SWER(Ze) relationship is derived for each event. Additionally, a SWER(Ze) relationship is derived for each synoptic classification by merging all events within each class. Two new types of SWER(Ze) relationships are derived from PIP measurements of bulk density and habit classification. These two physically-based SWER(Ze) relationships provided superior estimates of SWER when compared to the operational, event-specific, and synoptic SWER(Ze) relationships. For estimates of the event snow water equivalent total, the event-specific, synoptic, and best-performing operational SWER(Ze) relationships outperformed the physically-based SWER(Ze) relationship, although the physically-based relationships still performed well. This study recommends using the density or habit based SWER(Ze) relationships for microphysical studies, whereas, the other SWER(Ze) relationships are better suited towards hydrologic application.

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Sonja Murto
,
Lukas Papritz
,
Gabriele Messori
,
Rodrigo Caballero
,
Gunilla Svensson
, and
Heini Wernli

Abstract

In recent decades the Arctic has warmed faster than the global mean, especially during winter. This has been attributed to various causes, with recent studies highlighting the importance of enhanced downward infrared radiation associated with anomalous inflow of warm, moist air from lower latitudes. Here we study wintertime surface energy budget (SEB) anomalies over Arctic sea-ice on synoptic time scales, using ERA5 reanalyses (1979–2020). We introduce a new algorithm to identify areas with extreme positive daily-mean SEB anomalies and connect them to form spatio-temporal life-cycle events. Most of these events are associated with large-scale inflow from the Atlantic/Pacific Oceans, driven by poleward deflection of the storm track and blocks over northern Eurasia/Alaska. Events originate near the ice edge, where they have roughly equal contributions of net longwave radiation and turbulent fluxes to the positive SEB anomaly. As the events move further into the Arctic, SEB anomalies decrease due to weakening sensible and latent heat flux anomalies, while the surface temperature anomaly increases towards the peak of the events along with the downward longwave radiation anomaly. Due to these temporal and spatial differences, the largest SEB anomalies are not always related to strongest surface warming. Thus, studying temperature anomalies alone might not be sufficient to determine sea-ice changes. This study highlights the importance of turbulent fluxes in driving SEB anomalies and downward longwave radiation in determining local surface warming. Therefore, both processes need to be accurately represented in climate models.

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Stephen J. Lord
,
Xingren Wu
,
Vijay Tallapragada
, and
F.M. Ralph

Abstract

The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical weather forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the National Centers for Environmental Prediction (NCEP) Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble-variational (4DEnVar) data assimilation system. The control run (CTRL) used all of the routinely assimilated data and included data from 628 ARR dropsondes, whereas the denial run (DENY) excluded the dropsonde data. Results from 17 Intensive Observing Periods (IOPs) indicate a mixed impact for mean sea-level pressure and geopotential height over the Pacific North American (PNA) region in CTRL compared to DENY. The overall local impact over the U.S. West Coast and Gulf of Alaska for the 17 IOPs is neutral (−0.45%) for Integrated Vapor Transport (IVT), but positive for wind and moisture profiles (0.5-1.0%), with a spectrum of statistically significant positive and negative impacts for various IOPs. The positive dropsonde data impact on precipitation forecasts over U.S. West Coast domains appears driven, in part, by improved low-level moisture and wind fields at short-forecast lead times. Indeed, data gaps, especially for accurate and unbiased moisture profiles and wind fields, can be at least partially mitigated to improve U.S. West Coast precipitation forecasts.

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Meng Li
,
Jun A. Zhang
,
Leo Matak
, and
Mostafa Momen

Abstract

The momentum roughness length (z 0) significantly impacts wind predictions in weather and climate models. Nevertheless, the impacts of z 0 parameterizations in different wind regimes and various model configurations on the hurricane size, intensity, and track simulations have not been thoroughly established. To bridge this knowledge gap, a comprehensive analysis of 310 simulations of 10 real hurricanes using the Weather Research and Forecasting (WRF) model is conducted in comparison with observations. Our results show that the default z 0 parameterizations in WRF perform well for weak (category 1-2) hurricanes; however, they underestimate the intensities of strong (category 3-5) hurricanes. This finding is independent of model resolution or boundary layer schemes. The default values of z 0 in WRF agree with the observational estimates from dropsonde data in weak hurricanes while they are much larger than observations in strong hurricanes regime. Decreasing z 0 close to the values of observational estimates and theoretical hurricane intensity models in high wind regimes (≳ 45 m s-1) led to significant improvements in the intensity forecasts of strong hurricanes. A momentum budget analysis dynamically explained why the reduction of z 0 (decreased surface turbulent stresses) leads to stronger simulated storms.

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Jinfei Wang
,
Hao Luo
,
Lejiang Yu
,
Xuewei Li
,
Paul R. Holland
, and
Qinghua Yang

Abstract

Both the Southern Annular Mode (SAM) and the El Niño-Southern Oscillation (ENSO) are critical factors contributing to Antarctic sea ice variability on interannual time scales. However, their joint effects on sea ice are complex and remain unclear for each austral season. In this study, satellite sea ice concentration (SIC) observations and atmospheric reanalysis data are utilized to assess the impacts of combined SAM and ENSO on the seasonal Antarctic sea ice changes. The joint SAM-ENSO impacts on southern high-latitudes are principally controlled by the strength and position of the wave activity and associated atmospheric circulation anomalies affected by their interactions. In-phase events (La Niña/positive SAM and El Niño/negative SAM, hereinafter referred to as LN/pSAM & EN/nSAM) are characterized with an SIC dipole located in the Weddell/Bellingshausen Seas and Amundsen/Ross Seas, while out-of-phase events (El Niño/positive SAM and La Niña/negative SAM, hereinafter referred to as EN/pSAM & LN/nSAM) experience significant SIC anomalies in the Indian Ocean and western Pacific Ocean. Sea ice budget analyses are conducted to separate the dynamic and thermodynamic contributions inducing the sea ice intensification anomalies. The results show that in-phase intensification anomalies also display a pattern similar to the SIC dipole and are mainly driven by the direct thermodynamic forcing at the ice edge and thermodynamic responses to meridional sea ice drift in the inner pack, especially in autumn and winter. Dynamic processes caused by zonal sea ice drift also play an important role during out-of-phase conditions in addition to the same mechanisms during in-phase conditions.

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Anirudhan Badrinath
,
Luca Delle Monache
,
Negin Hayatbini
,
Will Chapman
,
Forest Cannon
, and
Marty Ralph

Abstract

A machine learning method based on spatial convolution to capture complex spatial precipitation patterns is proposed to identify and reduce biases affecting predictions of a dynamical model. The method is based on a combination of a classification and dual regression model approach using modified U-Net convolutional neural networks (CNN) to post-process daily accumulated precipitation over the US west coast. In this study, we leverage 34 years of high resolution deterministic Western Weather Research and Forecasting (West-WRF) precipitation reforecasts as training data for the U-Net CNN. The data is split such that the test set contains 4 water years of data that encompass characteristic west coast precipitation regimes: El Niño, La Niña, and dry and wet El Niño/Southern Oscillation (ENSO-neutral) water years. On the unseen 4-year data set, the trained CNN yields a 12.9-15.9% reduction in root mean square error (RMSE) and 2.7-3.4% improvement in Pearson correlation (PC) over West-WRF for lead times of 1-4 days. Compared to an adapted model output statistics correction, the CNN reduces RMSE by 7.4-8.9% and improves PC by 3.3-4.2% across all events. Effectively, the CNN adds more than a day of predictive skill when compared to West-WRF. The CNN outperforms the other methods also for the prediction of extreme events, which we define as the top 10% of events with the greatest average daily accumulated precipitation. The improvement over West-WRF’s RMSE/PC for these events is 19.8-21.0%/4.9-5.5% and MOS’s RMSE/PC is 8.8-9.7%/4.2-4.7%. Hence, the proposed U-Net CNN shows significantly improved forecast skill over existing methods, highlighting a promising path forward for improving precipitation forecasts.

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Ning Jiang
,
Minjie Yu
,
Bo Lu
,
Jeremy Cheuk-Hin Leung
, and
Congwen Zhu

Abstract

The persistence barrier (PB), one of the El Niño–Southern Oscillation (ENSO) properties, has exhibited a significant decadal variability, showing enhanced and weakened behavior before and after the late 1970s, respectively. In the present study, both the theoretical solution and the observations indicate that the variability of PB intensity is linearly proportional to the seasonal amplitude of ENSO growth rate, which accounts for the ENSO PB decadal variability. With further use of the Bjerknes–Jin (BJ) index analysis, we find that the decadal reduction in PB intensity since the late 1970s is mainly attributed to the mean advection and the thermocline feedback. In addition, the stronger spring thermal damping delayed the timing of PB in the 1980s and 1990s. Our study establishes a linear relationship between PB intensity and ENSO growth rate, which carries implications for understanding the ENSO predictability and the systematic changes in ENSO properties under climate change.

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