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Lauri Tuppi
,
Madeleine Ekblom
,
Pirkka Ollinaho
, and
Heikki Järvinen

Abstract

Numerical weather prediction models contain parameters that are inherently uncertain and cannot be determined exactly. It is thus desirable to have reliable objective approaches for estimation of optimal values and uncertainties of these parameters. Traditionally, the parameter tuning has been done manually, which can lead to the tuning process being a maze of subjective choices. In this paper we present how to optimize 20 key physical parameters in the atmospheric model Open Integrated Forecasting System (OpenIFS) that have a strong impact on forecast quality. The results show that simultaneous optimization of O(20) parameters is possible with O(100) algorithm steps using an ensemble of O(20) members; the results also show that the optimized parameters lead to substantial enhancement of predictive skill. The enhanced predictive skill can be attributed to reduced biases in low-level winds and upper-tropospheric humidity in the optimized model. We find that the optimization process is dependent on the starting values of the parameters that are optimized (starting from better-suited values results in a better model). The results show also that the applicability of the tuned parameter values across different model resolutions is somewhat limited because of resolution-dependent model biases, and we also found that the parameter covariances provided by the tuning algorithm seem to be uninformative.

Significance Statement

The purpose of this work is to show how to use algorithmic methods to optimize a weather model in a computationally efficient manner. Traditional manual model tuning is an extremely laborious and time-consuming process, so algorithmic methods have strong potential for saving the model developers’ time and accelerating development. This paper shows that algorithmic optimization is possible and that weather forecasts can be improved. However, potential issues related to the use of the optimized parameter values across different model resolutions are discussed as well as other shortcomings related to the tuning process.

Open access
James N. Marquis
,
Zhe Feng
,
Adam Varble
,
T. Connor Nelson
,
Adam Houston
,
John M. Peters
,
Jake P. Mulholland
, and
Joseph Hardin

Abstract

A lack of routine environmental observations located near deepening cumulus congestus clouds limits verification of important theorized and simulated updraft–environment interaction processes occurring during deep convection initiation (CI). We analyze radiosonde profiles collected during several hundred CI events near a mountain range in central Argentina during the CACTI field campaign. Statistical analyses illustrate environmental conditions supporting radar-observed CI outcomes that span a spectrum of convective cell depths, widths, and durations, as well as events lacking precipitating convection. Tested environmental factors include a large variety of sounding-derived measurements of CAPE, CIN, moisture, terrain-relative winds, vertical shear, and lifted parcel properties, with supplemental model reanalysis of background larger-scale vertical motion. CAPE and CIN metrics do not consistently differentiate CI success from failure. Only a few environmental factors contain consistent monotonic relationships among the spectrum of cloud depths achieved during CI: (i) the depth and strength of background ascent, and (ii) the component of low-level flow oriented parallel to the ridgeline. These metrics suggest that the ability of the surrounding flow to lift parcels to their LFC and terrain-modified flow are consistently relevant processes for CI. Low- to midlevel relative humidity strongly discriminated between CI and non-CI events, likely reflecting entrainment-driven dilution processes. However, we could not confidently conclude that relative humidity similarly discriminated robust from marginal CI events. Circumstantial evidence was found linking cell width, an important cloud property governing the probability of CI, to LCL height, boundary layer depth, depth and magnitude of the CIN layer, and ambient wind shear.

Open access
Philippe P. Papin
,
Carolyn A. Reynolds
, and
Matthew A. Janiga

Abstract

Potential vorticity streamers (PVSs) are elongated filaments of high-PV air near the tropopause. In the warm season, anticyclonic Rossby wave breaking (AWB) produces enhanced PVS activity, which in turn modifies the equatorward tropical environment by enhancing vertical wind shear (VWS). This enhanced VWS can play an important role in suppressing nearby tropical cyclone (TC) activity. Given the important role that PVSs play in modifying their local environment, forecasts of PVS activity on subseasonal time scales may also influence forecasts of TC activity. This study uses Navy Earth System Prediction Capability (Navy ESPC) 45-day forecasts initialized during boreal summer 2009–15 to investigate subseasonal predictability of PVSs and TCs in the North Atlantic. PVSs are identified on the 350-K isentropic surface bounded by the 2 PV unit (PVU; 1 PVU = 10−6 K kg−1 m2 s−1) contour and defined as the high-PV trough axis downstream of the AWB axis. TCs are identified in the forecasts using the TempestExtremes detection algorithm that tracks warm-core lows. PVS and TC activity metrics that sum the number and intensity of events for a given time period are also computed. We first use skill scores and mean-state biases to determine the typical predictability of PVS activity, and then subselect high- and low-PVS-activity forecasts to determine how PVS forecast errors impact TC activity forecast errors. Results show that PVS activity can modulate TC activity at subseasonal time scales, with over-forecasted PVS activity corresponding to underestimated forecasts of TC activity and vice versa. This inverse correlation is consistent with enhanced VWS occurring equatorward of PVS troughs in the high-PVS forecasts.

Free access
Deepak Gopalakrishnan
,
Sourav Taraphdar
,
Olivier M. Pauluis
,
Lulin Xue
,
R. S. Ajayamohan
,
Noor Al Shamsi
,
Sisi Chen
,
Jared A. Lee
,
Wojciech W. Grabowski
,
Changhai Liu
,
Sarah A. Tessendorf
, and
Roy M. Rasmussen

Abstract

This study investigates the structure and evolution of a summertime convective event that occurred on 14 July 2015 over the Arabian region. We use the WRF Model with 1-km horizontal grid spacing and test three PBL parameterizations: the Mellor–Yamada–Nakanishi–Niino (MYNN) scheme; the Asymmetrical Convective Model, version 2, (ACM2) scheme; and the quasi-normal scale-elimination (QNSE) scheme. Convection initiates near the Al Hajar Mountains of northern Oman at around 1100 local time (LT; 0700 UTC) and propagates northwestward. A nonorographic convective band along the west coast of the United Arab Emirates (UAE) develops after 1500 LT as a result of the convergence of cold pools with the sea breeze from the Arabian Gulf. The model simulation employing the QNSE scheme simulates the convection initiation and propagation well. Although the MYNN and ACM2 simulations show convective initiation near the Al Hajar Mountains, they fail to simulate the development of the convective band along the UAE west coast. The MYNN run simulates colder near-surface temperatures and a weaker sea breeze, whereas the ACM2 run simulates a stronger sea breeze but a drier lower troposphere. Sensitivity simulations using horizontal grid spacings of 9 and 3 km show that lower-resolution runs develop broader convective structures and weaker cold pools and horizontal wind divergence, affecting the development of convection along the west coast of the UAE. The 1-km run using the QNSE PBL scheme realistically captures the sequence of events that leads to the moist convection over the UAE and adjacent mountains.

Open access
Chi-June Jung
and
Ben Jong-Dao Jou

Abstract

Severe rainfall has become increasingly frequent and intense in the Taipei metropolitan area. A complex thunderstorm in the Taipei Basin on 14 June 2015 produced an extreme rain rate (>130 mm h−1), leading to an urban flash flood. This paper presents storms’ microphysical and dynamic features during the organizing and heavy rain stages, mainly based on observed polarimetric variables in a Doppler radar network and ground-based raindrop size distribution. Shallower isolated cells in the early afternoon characterized by big raindrops produced a rain rate > 10 mm h−1, but the rain showers persisted for a short time. The storm’s evolution highlighted the behavior of merged convective cells before the heaviest rainfall (exceeding 60 mm within 20 min). The columnar features of differential reflectivity (Z DR) and specific differential phase (K DP) became more evident in merged cells, which correlated with the broad distribution of upward motion and mixed-phase hydrometeors. The K DP below the environmental 0°C level increased toward the ground associated with the melted graupel and resulted in subsequent intense rain rates, showing the contribution of the ice-phase process. Due to the collision–breakup process, the highest concentrations of almost all drop sizes and smaller mass-weighted mean diameter occurred during the maximum rainfall stage.

Open access
Zied Ben Bouallègue
,
Fenwick Cooper
,
Matthew Chantry
,
Peter Düben
,
Peter Bechtold
, and
Irina Sandu

Abstract

Based on the principle “learn from past errors to correct current forecasts,” statistical postprocessing consists of optimizing forecasts generated by numerical weather prediction (NWP) models. In this context, machine learning (ML) offers state-of-the-art tools for training statistical models and making predictions based on large datasets. In our study, ML-based solutions are developed to reduce forecast errors of 2-m temperature and 10-m wind speed of the ECMWF’s operational medium-range, high-resolution forecasts produced with the Integrated Forecasting System (IFS). IFS forecasts and other spatiotemporal indicators are used as predictors after careful selection with the help of ML interpretability tools. Different ML approaches are tested: linear regression, random forest decision trees, and neural networks. Statistical models of systematic and random errors are derived sequentially where the random error is defined as the residual error after bias correction. In terms of output, bias correction and forecast uncertainty prediction are made available at any point from locations around the world. All three ML methods show a similar ability to capture situation-dependent biases leading to noteworthy performance improvements (between 10% and 15% improvement in terms of root-mean-square error for all lead times and variables), and a similar ability to provide reliable uncertainty predictions.

Open access
Željka Stone
,
G. R. Alvey III
,
J. P. Dunion
,
M. S. Fischer
,
D. J. Raymond
,
R. F. Rogers
,
S. Sentić
, and
J. Zawislak

Abstract

As a part of the Tropical Cyclone Rapid Intensification Project (TCRI), observations were made of the rapid intensification of Hurricane Sally (2020) as it passed over the Gulf of Mexico. High-altitude dropsondes and radar observations from NOAA’s Gulfstream IV, radar observations from WP-3D aircraft, the WSR-88D ground radar network, satellite images, and satellite-detected lightning strikes are used to apply recently developed theoretical knowledge about tropical cyclone intensification. As observed in many other tropical cyclones, strong, bottom-heavy vertical mass flux profiles are correlated with low (but positive) values of low- to midlevel moist convective instability along with high column relative humidity. Such mass flux profiles produce rapid spinup at low levels and the environmental conditions giving rise to them are associated with an intense midlevel vortex. This low-level spinup underneath the midlevel vortex results in the vertical alignment of the vortex column, which is a key step in the rapid intensification process. In the case of Sally, the spinup of the low-level vortex resulted from vorticity stretching, while the spinup of the midlevel vortex at 6 km resulted from vorticity tilting produced by the interaction of convective ascent with moderate vertical shear.

Significance Statement

The purpose of this study is to investigate the rapid intensification of Hurricane Sally as it was approaching the Florida Panhandle. We do that by analyzing an unprecedented dataset from the NOAA WP-3D and Gulfstream-IV aircraft, together with ground-based radar and satellite data. We find that both the dynamics (vorticity structure and evolution) and thermodynamics (instability index, saturation fraction, heating/mass flux profiles) need to be considered in diagnosing intensification processes. Further field projects with continuous high-altitude dropsondes and research are needed to see if these are applicable to other reformation events as well as genesis.

Open access
Hailing Zhang
,
Ying-Hwa Kuo
, and
Sergey Sokolovskiy

Abstract

The local spectral width (LSW) of a radio occultation (RO) observation in impact parameter representation is a useful parameter for providing information on the uncertainty associated with the RO bending angle measurement. The LSW can potentially be used to specify the bending angle observation error (BaOE) in the lower troposphere for each individual sounding. This study assesses the usefulness and limitations of LSW in representing BaOE for a global data assimilation system. A two-step scheme is proposed to derive profile-dependent BaOE from LSW. Since the LSW-based BaOE varies with each individual RO observation, it is here designated as a dynamic BaOE (DBOE) in contrast to the traditional statistics-based BaOE specification. A benchmark control run and two sensitivity experiments are conducted with continuous cycling data assimilation using the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) and Global Forecast System (GFS). The usefulness and impact of the LSW-based DBOE are evaluated using radiosonde observations and global analyses. Results show that DBOE is able to improve the assimilation of RO data, leading to better forecast skill scores. Another experiment, in which the GSI statistical observation error of the benchmark run is replaced by the average of LSW-based DBOE, shows that the ability to assign larger weighting for high-quality observation and lower weighting for low-quality observation is the key factor for the success of the LSW-based DBOE.

Open access
Taiga Tsukada
and
Takeshi Horinouchi

Abstract

Estimation of the radius of maximum wind (RMW) of tropical cyclone (TC) is helpful for the disaster prevention and mitigation. If RMWs are estimated from infrared (IR) imagery taken by geostationary meteorological satellites, their estimation is available densely in time, regardless of the ocean basin. Kossin et al. showed that when TCs have clear eyes, the eye radii estimated from IR images have a high correlation with the RMW estimated from aircraft reconnaissance. The regression of the former onto that latter was shown to have a mean absolute error (MAE) of 4.7 km. We revisit the IR-based RMW estimation by using C-band synthetic aperture radar (SAR) sea surface wind estimates. The criteria for selecting clear-eye cases are simplified. The MAE of the Kossin et al. method is found to be smaller than previously suggested: 3.1 km when the proposed relation is used and 2.7 km when the regression is revised with the SAR-measured RMWs. We further propose an improvement of the IR-based method to estimate the eye radii. The resultant MAE is shown to be 1.7 km, which indicates that the IR-based RMW estimation is more accurate than has been suggested. A strong correlation between eyewall slope and eye size is confirmed. We also investigated cloud features in the eye that may be closely related to RMW and wind structure around RMW. Potential applications of highly accurate RMW estimation are discussed.

Significance Statement

The radius of maximum wind (RMW) of tropical cyclone (TC) is an important factor for TC intensity estimation and disaster prevention. A previous study suggested that the RMWs of TCs with clear eyes can be estimated from geostationary satellite images at a mean absolute error (MAE) of 4.7 km. Here we improved the method, reducing the MAE by more than one-half. Since the method does not require aircraft or satellite in low Earth orbit, it helps TC monitoring at high frequency. The method can also improve initialization of models used to predict TC hazards and further our physical understanding and the climatology of the wind structures near the centers of TCs.

Open access
Clemente Lopez-Bravo
,
Claire L. Vincent
,
Yi Huang
, and
Todd P. Lane

Abstract

A West Sumatra squall line occurred on 10 January 2016, with a clear offshore propagation of convection. Satellite-derived products from Himawari-8 Advanced Himawari Imager and the Geostationary Cloud Algorithm Testbed Geocat are used to investigate the westward propagation of cloudiness from Sumatra to the Indian Ocean with a lifetime of 1.5 days. A convective mask based on deep convective cell detection and a cell-tracking algorithm are used to estimate the propagation speed of the cloud system. Two distinct mesoscale convective responses are identified: 1) a rapid development in South Sumatra is influenced by the convective environment over the Indian Ocean. The propagation speed is estimated to be ∼5 m s−1 within the first 200 km from the coast. This speed is consistent with density currents. In contrast, 2) the coupling to the inertia–gravity wave is only evident for the northwest of Sumatra with speeds of ∼12 m s−1. The analysis of brightness temperature from the 10.4-μm spectral band and cloud-top temperature showed that the lifetime of the squall line is approximately 30 h with a propagating distance of ∼1000 km. Retrieved cloud properties and tracking of the offshore propagation indicated that the cloud structure consisted of multiple types of cells, propagating as envelopes of convection, and revealed the influence of large-scale variability of the Indian Ocean. Filtered OLR anomalies, satellite-derived rainfall, moisture flux convergence, and background winds flow around Sumatra are used to explore the effects of Kelvin wave activity that likely influenced the lifetime of the squall line.

Free access