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Carolyn A. Reynolds, William Crawford, Andrew Huang, Neil Barton, Matthew A. Janiga, Justin McLay, Maria Flatau, Sergey Frolov, and Clark Rowley

Abstract

High-fidelity analyses and forecasts of integrated vapor transport (VT) are central to the study of Earth’s hydrological cycle as well as high-impact phenomena such as monsoons and atmospheric rivers. The impact of the in-line analysis correction-based additive inflation (ACAI) on IVT biases and forecast errors is examined within the Navy Earth System Prediction Capability (Navy ESPC) global coupled system. The ACAI technique uses atmospheric analysis corrections from the data assimilation system to approximate model bias and as a representation of stochastic model error to simultaneously reduce systematic and random errors and improve ensemble performance. ACAI reduces the global average magnitude of the 7- and 14-day IVT bias by 16%–17% during Northern Hemisphere summer, reaching 70% reductions in some tropical regions. The global average IVT bias reduction is similar to the bias reduction for low-level wind speed bias and considerably smaller than the bias reduction in total precipitable water. The localized regions where ACAI increases IVT bias occur where the control IVT biases change sign and structure with increasing forecast lead time, such as the South Asian monsoon region. Substituting analyzed wind or moisture fields for the forecast fields when calculating the forecast IVT confirms that, on average, wind errors dominate the IVT error calculation in the tropics, although wind and moisture error contributions are comparable in the extratropics. The existence of regions where using either analyzed winds or analyzed moisture increases IVT bias or mean absolute error reveals areas with compensating errors.

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David I. Duncan, Niels Bormann, Alan J. Geer, and Peter Weston

Abstract

Radiances from microwave temperature sounders have been assimilated operationally at ECMWF for two decades, but observations significantly affected by clouds and precipitation have been screened out. Extending successful assimilation beyond clear-sky scenes is a challenge that has taken several years of development to achieve. In this paper we describe the all-sky treatment of AMSU-A, which enables greater numbers of temperature sounding radiances to be used in meteorologically active parts of the troposphere. Successful all-sky assimilation required combining lessons learned from the clear-sky assimilation of AMSU-A with the approach initially developed for humidity-sensitive microwave radiances. This concerned particularly observation thinning, error modeling, and variational quality control. As a result of the move to all-sky assimilation, the forecast impact of AMSU-A now replicates and exceeds that of the previous clear-sky usage. This is shown via trials in comparison to the current ECMWF assimilation system, judged with respect to forecast scores and background fits to independent observations. Persistently cloudy regions and phenomena such as tropical cyclones are better sampled when assimilating AMSU-A in all-sky conditions, causing an increase of about 13% in used channel-5 radiances globally. These impacts are explored, with an emphasis on tropical cyclones in the 2019 season. Independent observations provide consistent evidence that representation of humidity is improved, for example, while extratropical Z500 forecasts are improved by about 0.5% out to at least day 2. On the strength of these results, assimilation of AMSU-A moved to all-sky conditions with the upgrade to IFS cycle 47R3 in October 2021.

Open access
Mathieu Lachapelle and Julie M. Thériault

Abstract

Freezing rain and ice pellets are particularly difficult to forecast when solid precipitation is completely melted aloft. This study addresses this issue by investigating the processes that led to a long-duration ice pellet event in Montreal, Québec, Canada, on 11–12 January 2020. To do so, a benchmark model initialized with ERA5 data is used to show that solid precipitation was completely melted below the melting layer, which discards partial melting from the possible ice pellet formation processes. Macro photography of precipitation reveals that small columnar crystals (∼200 μm) and ice pellets occurred simultaneously for more than 10 h. The estimation of ice crystal number concentration using macro photographs and laser-optical disdrometer data suggests that all supercooled drops could have refrozen by contact freezing with ice crystals. Rimed ice pellets also indicate ice supersaturation in the subfreezing layer. Given these observations, the formation of ice pellets and ice crystals was probably promoted by secondary ice production and the horizontal advection of ice crystals below the melting layer, as we illustrate using a conceptual model. Overall, these findings demonstrate how ice nucleation processes at temperatures near 0°C can drastically change the precipitation phase and the impact of a storm.

Significance Statement

Ice pellets are generally formed when snow particles partially melt while falling through a warm layer aloft before completely refreezing in a cold layer closer to the surface. Ice pellets can also be formed when snow particles completely melt aloft, but freezing rain is often produced in such conditions. On 11–12 January 2020, ice pellets were produced during more than 10 h in Montreal, Quebec, Canada. Macro photographs of the precipitation particles show that ice pellets occurred simultaneously with small ice crystals. Most of the ice pellets were produced while snow particles were completely melted aloft. The supercooled drops probably refroze due to collisions with the ice crystals that could have been advected by the northeasterly winds near the surface.

Open access
Roland Potthast, Klaus Vobig, Ulrich Blahak, and Clemens Simmer

Abstract

We investigate the assimilation of nowcasted information into a classical data assimilation cycle. As a reference setup, we employ the assimilation of standard observations such as direct observations of particular variables into a forecasting system. The pure advective movement extrapolation of observations as a simple nowcasting (NWC) is usually much better for the first minutes to hours, until outperformed by numerical weather prediction (NWP) based on data assimilation. Can nowcasted information be used in the data assimilation cycle? We study both an oscillator model and the Lorenz 63 model with assimilation based on the localized ensemble transform Kalman filter (LETKF). We investigate and provide a mathematical framework for the assimilation of nowcasted information, approximated as a local tendency, into the LETKF in each assimilation step. In particular, we derive and discuss adequate observation error and background uncertainty covariance matrices and interpret the assimilation of nowcasted information as assimilation with an H 1-type metric in observation space. Further, we show numerical results that prove that nowcasted information in data assimilation has the potential to significantly improve model based forecasting.

Open access
Masih Eghdami, Ana P. Barros, Pedro A. Jiménez, Timothy W. Juliano, and Branko Kosovic

Abstract

Accurate representation of heterogeneous surface layer processes is essential for numerical weather prediction (NWP) with sub-kilometer grid spacing. NWP models such as the Weather Research and Forecasting (WRF) Model generally use second-moment turbulent models for parameterizing the planetary boundary layer (PBL). The most common parameterizations follow Mellor–Yamada and account for the vertical turbulent mixing only; that is, standard PBL parameterizations are one-dimensional (1DPBL). The horizontal diffusion of momentum is parameterized based on Smagorinsky’s model for numerical stability. Although the combination of 1DPBL and 2D Smagorinsky parameterizations is successful at coarse grid resolutions (e.g., grid-size dx ∼ 12–2 km), it does not represent well the effect of horizontal turbulence as gridcell size decreases (<1 km). To reconcile the representation of vertical and horizontal turbulent mixing, a full three-dimensional PBL scheme (3DPBL) based on the Mellor–Yamada model was implemented in WRF. The 3DPBL uses the horizontal and vertical turbulent fluxes diagnosed from the flow gradients to handle the turbulent mixing. These gradients cannot be directly calculated near the surface. Therefore, the 3DPBL parameterization is coupled herein to a second-order diagnostic model of the three-dimensional turbulent fluxes in the surface layer. Several adjustments to the original Mellor–Yamada model, including a modified length scale, were introduced to capture flow anisotropy and dependence on stability conditions. The results are compared against data from the Wind Forecast Improvement Project 2 (WFIP2) for different weather regimes and using different grid resolutions to examine stability and scale dependency.

Open access
Paolo Giani, Marc G. Genton, and Paola Crippa

Abstract

Modeling atmospheric turbulence in the convective boundary layer is challenging at kilometer and subkilometer resolutions, as the horizontal grid spacing approaches the size of the most energetic turbulent eddies. In this range of resolutions, termed terra incognita or gray zone, partially resolved convective structures are grid dependent and neither traditional 1D mesoscale parameterizations nor 3D large-eddy simulations closures are theoretically appropriate. Leveraging on a new set of one-way nested, full-physics multiscale numerical experiments, we quantify the magnitude of the errors introduced at gray zone resolutions in a real-case application and we provide new perspectives on recently proposed modeling approaches. The new set of experiments is forced by real-time-varying boundary conditions, spans a wide range of scales, and includes traditional 1D schemes, 3D closures, scale-aware parameterizations, and strategies to suppress resolved convection at gray zone resolutions. The study area is Riyadh (Saudi Arabia), where deep CBLs develop owing to strong convective conditions. Detailed analyses of our experiments, including validation with radiosonde data, calculations of spectral features, and partitioning of turbulent fluxes between resolved and subgrid scales, show that (i) grid-dependent convective structures entail minor impacts on the first-order characteristics of the fully developed boundary layer due to some degree of implicit scale awareness of 1D parameterizations and (ii) 3D closures and scale-aware schemes outperform traditional 1D schemes especially in the surface layer, among other findings. The new suite of experiments provides a benchmark of real simulations that can be extended to assess how new turbulence closures perform at gray zone resolutions.

Significance Statement

As recent advances in high-performance computing are leading to a new era in numerical simulations, regional atmospheric models can now increase their resolution to the widely unexplored kilometer and subkilometer range. While increasing the resolution of atmospheric models is desirable to (i) have more realistic weather and air quality predictions and (ii) better represent boundary conditions for microscale models, kilometer and subkilometer grid spacings pose some theoretical challenges that need to be addressed by the atmospheric modeling community. In this work we run a set of numerical experiments for a real case study that aim to offer new perspectives on recently developed modeling strategies and identify the most promising directions that should be investigated by follow-up studies.

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Zhilin Zeng and Donghai Wang

Abstract

Extreme short-term hourly rainfall accumulations are shown to be commonly attendant with rotation-featured mesovortices. This study aims to document the detailed aspects of mesovortex-related extreme rain rates in a record-breaking event that occurred over South China on 7 May 2017 through high-resolution observations, and to reveal how the mesoscale storm contributes to the extreme rain rates, with special attention given to the minute rain-rate enhancement episode (MREE). The event accumulation possesses highly local distribution, with a spatial rainfall core. Extreme rain rates are dictated by a meso-γ-scale storm, and it is found that the observed rain rate is well correlated to storm-related maximum reflectivity in the lowest 2 km above ground level (AGL) as well as its reflectivity centroid (i.e., reflectivity factor exceeding 50 dBZ) depth. The cross section reveals that the storm structure evolves progressively into a single centroid, which subsequently descends to the near surface, resulting in the rain-rate peak (4.8 mm min−1). Extremely weak environmental mean flow interacts with the near-opposite storm propagation that determines the slow storm movement, prolonging local rainfall. Rainfall-induced cold outflow surges ahead unevenly, leading to an inhomogeneous mesoscale outflow boundary (IMOB) at low levels. A mesovortex subsequently develops along the IMOB in the lowest ∼5.5 km AGL, with a horizontal radius of 1.5–3 km. A mesoscale low-level jet observed over the upstream of the storm increases the low-level shear despite short duration, which provide potential dynamics for the storm and mesovortex development. These results help us better understand the generation of extreme rain rates in small spatiotemporal scale.

Significance Statement

Despite many extreme short-duration rainfall events linking to low-level rotation phenomenon, we are still exploring how these rotations develop within extreme-rainfall-producing storms. While past studies have found that these low-level rotations within such storms are conducive to enhancing rainfall, rotation characteristics are less understood. In this study, we examine how the rotations develop during an episode of observed-rainfall rise. The results reveal that the cold airflows resulted from the storm-inducing rainfall impinge unevenly on warm moist southerly wind, leading to such low-level rotation. The rainfall observed per minute is found to associate well with low-level rotation that develops upward. These results further motivate exploration of extreme short-duration rainfall and storm-related rotation.

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Jeffrey L. Anderson

Abstract

A general framework for deterministic univariate ensemble filtering is presented. The framework fits a continuous prior probability density function (PDF) to the prior ensemble. A functional representation for the observation likelihood is combined with the prior PDF to get a continuous analysis (posterior) PDF. Cumulative distribution functions for the prior and analysis are also required. The key innovation is that an analysis ensemble is computed so that the quantile of each ensemble member is the same as its prior quantile. Many choices for the prior PDF family and the likelihood function are described. A choice of normal prior with normal likelihood is equivalent to the ensemble adjustment Kalman filter. Some other choices for the prior include gamma, inverse gamma, beta, beta prime, lognormal, and exponential distributions. Both prior distributions and likelihoods can be defined over a set of intervals giving additional flexibility that can be used to implement methods like a Huber likelihood for observations with occasional outliers. Priors and likelihoods can also be defined as sums of distributions allowing choices like bivariate normals or kernel filters. Empirical distributions, for instance piecewise linear approximations to arbitrary PDFs and functions can be used. Another empirical choice leads to the rank histogram filter. Results here are univariate and can be used to compute increments for observed variables or marginal distributions for any variable for a reanalysis. Linear regression of increments can be used to update state variables in a serial filter to build a comprehensive data assimilation system. Part 2 will discuss other methods for extending the framework to multivariate data assimilation.

Significance Statement

Data assimilation is used to combine information from model forecasts with subsequent observations to obtain better estimates of the current state of the atmosphere or other parts of the Earth system. Ensemble data assimilation uses a number of forecasts to get more information about uncertainty. A new method allows much more flexibility in the assumptions that must be made when doing ensemble data assimilation. As an example, the method can be better for quantities that are bounded like the amount of an atmospheric trace pollutant.

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Terrence J. Corrigan Jr. and Steven Businger

Abstract

A series of extreme cloudbursts occurred on 14 April 2018 over the northern slopes of the island of Kaua‘i, Hawaii. The storm inundated some areas with 1262 mm (∼50 in.) of rainfall in a 24-h period, eclipsing the previous 24-h U.S. rainfall record of 1100 mm (42 in.) set in Texas in 1979. Three periods of intense rainfall are diagnosed through detailed analysis of National Weather Service operational and special datasets. On the synoptic scale, a slowly southeastward propagating trough aloft over a deep layer of low-level moisture (>40 mm of total precipitable water) produced prolonged instability over Kaua‘i. Enhanced northeast to east low-level flow impacted Kaua‘i’s complex terrain, which includes steep north- and eastward-facing slopes and cirques. The resulting orographic lift initiated deep convection. The wind profile exhibited significant shear in the troposphere and streamwise vorticity within the convective storm inflow. Evidence suggests that large directional shear in the boundary layer, paired with enhanced orographic vertical motion, produced rotating updrafts within the convective storms. Mesoscale rotation is manifest in the radar data during the latter two periods, and reflectivity cores are observed to propagate both to the left and to the right of the mean shear, which is characteristic of supercells. The observations suggest that the terrain configuration in combination with the wind shear separates the area of updrafts from the downdraft section of the storm, resulting in almost continuous heavy rainfall over Waipā Garden.

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Kyle M. Nardi, Colin M. Zarzycki, Vincent E. Larson, and George H. Bryan

Abstract

Recent studies have demonstrated that high-resolution (∼25 km) Earth System Models (ESMs) have the potential to skillfully predict tropical cyclone (TC) occurrence and intensity. However, biases in ESM TCs still exist, largely due to the need to parameterize processes such as boundary layer (PBL) turbulence. Building on past studies, we hypothesize that the depiction of the TC PBL in ESMs is sensitive to the configuration of the PBL parameterization scheme, and that the targeted perturbation of tunable parameters can reduce biases. The Morris one-at-a-time (MOAT) method is implemented to assess the sensitivity of the TC PBL to tunable parameters in the PBL scheme in an idealized configuration of the Community Atmosphere Model, version 6 (CAM6). The MOAT method objectively identifies several parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBB) scheme that appreciably influence the structure of the TC PBL. We then perturb the parameters identified by the MOAT method within a suite of CAM6 ensemble simulations and find a reduction in model biases compared to observations and a high-resolution, cloud-resolving model. We demonstrate that the high-sensitivity parameters are tied to PBL processes that reduce turbulent mixing and effective eddy diffusivity, and that in CAM6 these parameters alter the TC PBL in a manner consistent with past modeling studies. In this way, we provide an initial identification of process-based input parameters that, when altered, have the potential to improve TC predictions by ESMs.

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