Description: We study the dispersion properties of three choices for the buoyancy space in a mixed finite element discretization of geophysical fluid flow equations. The problem is analogous to that of the staggering of the buoyancy variable in finite difference discretizations. Discrete dispersion relations of the two-dimensional linear gravity wave equations are computed. By comparison with the analytical result, the best choice for the buoyancy space basis functions is found to be the horizontally discontinuous, vertically continuous option. This is also the space used for the vertical component of the velocity. At lowest polynomial order, this arrangement mirrors the Charney-Phillips vertical staggering known to have good dispersion properties in finite difference models. A fully discontinuous space for the buoyancy corresponding to the Lorenz finite difference staggering at lowest order gives zero phase velocity for high vertical wavenumber modes. A fully continuous space, the natural choice for scalar variables in a mixed finite element framework, with degrees of freedom of buoyancy and vertical velocity horizontally staggered at lowest order, is found to entail zero phase velocity modes at the large horizontal wavenumber end of the spectrum. Corroborating the theoretical insights, numerical results obtained on gravity wave propagation with fully continuous buoyancy highlight the presence of a computational mode in the poorly resolved part of the spectrum that fails to propagate horizontally. The spurious signal is not removed in test runs with higher order polynomial basis functions. Runs at higher order also highlight additional oscillations, an issue that is shown to be mitigated by partial mass-lumping. In light of the findings and with a view to coupling the dynamical core to physical parametrizations that often force near the horizontal grid scale, the use of the fully continuous space should be avoided in favour of the horizontally discontinuous, vertically continuous space.

Description: This article describes the results of assimilation experiments of dual-polarization (DPOL) radar observations in AROME model with its 3D-Var assimilation system. The assimilation of dual-polarization observations is derived from the current operational assimilation method for radar reflectivities. It has two steps (1D+3D-Var): Relative humidity pseudo-observations are first retrieved from observed profiles of reflectivity ( Z hh ), with or without attenuation correction, from differential reflectivity ( Z dr ), from specific differential phase ( K dp ), or from a combination of Z hh , Z dr and K dp , through a unidimensional (1D) Bayesian inversion. The retrievals are then assimilated in the AROME 3D-Var system. In case of attenuation, the values of the relative humidity profiles retrieved with uncorrected reflectivity were found lower in average than with attenuation-corrected reflectivity or K dp , while for large values of K dp , both methods using K dp produced the profiles with the largest values of relative humidity in convective regions. Assimilation experiments were conducted for two convective cases. They demonstrated the benefit of attenuation correction and of the assimilation of K dp in the model analysis. The humidity increments were found higher in convective regions when correcting for attenuation, or when assimilating K dp . Furthermore, the impact of DPOL observations on precipitation forecasts was found slightly positive for the periods of intense convection.

Description: Despite significant progress made in snowfall estimation from space, methods utilizing passive microwave measurements continue to be plagued by low detectability compared to those that estimate rainfall. This paper presents a hybrid snowfall detection algorithm that combines the output from a statistical algorithm utilizing satellite passive microwave measurements with the output from a statistical algorithm trained with in-situ data that uses meteorological variables derived from a global forecast model as predictors. The satellite algorithm computes the probability of snowfall over land using logistic regression and the principal components of the high frequency brightness temperature measurements at AMSU/MHS and ATMS channel frequencies 89 GHz and above. In a separate investigation, analysis of modelled data derived from NOAA’s Global Forecast System (GFS) showed that cloud thickness and relative humidity at 1- to 3-km height were the best predictors of snowfall occurrence. A statistical logistical regression model that combined cloud thickness, relative humidity and vertical velocity was selected among statistically significant variants as the one with the highest overall classification accuracy. Next, the weather-based and satellite model outputs were combined in a weighting scheme to produce a final probability of snowfall output, which was then used to classify a weather event as “snowing” or “no snowing” based on an a-priori threshold probability. Statistical analysis indicated that a scheme with equal weights applied to the weather-based and satellite model significantly improved satellite snowfall detection. Example applications of the hybrid algorithm over continental US demonstrated the improvement for a major snowfall event and for an event dominated by lighter snowfall.

Description: This study examined the vertical gradient of radar reflectivity below the detected bright band in stratiform regions from the tropics to the extratropical latitudes using data from the Ku-band (13.6 GHz) precipitation radar onboard the Global Precipitation Measurement (GPM) Core Observatory. Stratiform precipitation profiles with reflectivity decreasing (increasing) from the melting level toward the surface occur frequently in the tropical ocean (mid- and high latitude oceans). High fractions of downward increasing stratiform pixels are found over the North Pacific Ocean throughout the year and over East Asia except for winter. In contrast, the North American continent and the adjacent North Atlantic Ocean are characterized by low fractions of downward increasing pixels during summer. The difference is consistent with the dominant type of convection over East Asia (warm-type clouds) and over the North American continent (cold-type clouds). Even in the tropical oceans such as the Atlantic and eastern Pacific intertropical convergence zones, there are some areas with moderate fractions of downward increasing stratiform pixels where the warm rain process dominates. The downward reduction of reflectivity in the stratiform region of MCSs is obviously due to evaporation, which is a function of lower-tropospheric relative humidity. The downward increase of reflectivity in stratiform regions over the mid-latitude oceans appears the result of raindrop growth. This is achieved via the collection of cloud droplets while falling through low-level clouds produced by large-scale vertical motion in the lower troposphere due to large-scale convergence associated with synoptic-scale systems.

Description: Southern Africa (SA) and Eastern Africa (EA) experienced a sequence of severe droughts in December-January-February (SA DJF) 2015-16, October-November-December (EA OND) 2016 and March-April-May, 2017 (EA MAM). This sequence contributed to severe food insecurity. While climate variability in these regions is very complex, the goal of this study is to analyze the potential role played by unusually warm Indo-Pacific SS, where unusual is defined as a 1-in-6 year event. We use observed sea surface temperatures (SST) and satellite-gauge rainfall observations, a 20-member ensemble of Community Atmospheric Model version 5.1 simulations (CAM5), and a 40-member ensemble of climate change simulations from the Community Earth Systems Model version 1 (CESM1) Large Ensemble Community Project (LENS) to explore climate conditions associated with warm events identified based on eastern and western Pacific sea surface temperatures (SST). Our analysis suggests that strong El Niños may be followed by warm western Pacific SST conditions, which can lead to creating conditions conducive for successive and potentially predictable droughts in SA DJF, EA OND and EA MAM. We show that different regions of warm SST appear related to recent droughts – SA DJF: Niño 3.4, EA OND: Western Equatorial Pacific (WEP), and EA MAM: Western North Pacific (WNP). For DJF and MAM, respectively, the CAM5 model driven with observed SST and the same model driven within a climate change experiment indicate that warmer El Niños and WNP events produce more intense atmospheric responses, potentially associated with more severe droughts. OND climate seems to be strongly influenced by the Indian Ocean Dipole, which corresponds to some WEP events. We suggest the extreme SST events responsible for 2015-17 droughts are likely to reoccur, thus humanitarian agencies should consider multiyear drought and substantial food insecurity in SA and EA.

Description: This study assesses the medium-range flow-dependent forecast skill of Euro-Atlantic weather regimes: the positive and negative phases of the North Atlantic Oscillation (NAO+ and NAO–), Atlantic ridge (ATLR), and Euro-Atlantic blocking (EABL), for extended winters (November–March) in the periods 2006/07–2013/14 and 1985/86–2013/14 using The Interactive Grand Global Ensemble (TIGGE) and the National Oceanic and Atmospheric Administration (NOAA)’s Global Ensemble Forecasting System (GEFS) reforecast datasets, respectively. The models show greater-than-observed (smaller-than-observed) frequencies of NAO– and ATLR (NAO+) with forecast lead time. The increased frequency of NAO– is not due to its excess persistence but due to more frequent transitions mainly from ATLR, but also from NAO+. In turn, NAO+ is under-persistent. The models show the highest probabilistic skill for forecasts initialised on NAO– and the NAO– forecasts during the TIGGE period. However, the GEFS reforecast during the period 1985/86–2013/14 revealed that these recent high skills reflect the occurrence of four long-lasting (〉30 days) NAO– events in 2009/10–2013/14 and that the skill for forecasts initialised on NAO– before 2009/10 (the longest duration was 22 days and the second-longest duration was 16 days) was the lowest. The longer the NAO– events persist, the higher the skill of forecasts initialised on NAO–. The skill dependency on regime durations is less clearly observed for the other regimes. In addition, the GEFS reforecast also revealed that the highest skill of the NAO– forecasts during the period 1985/86–2013/14 is attributed to the higher skill of the NAO– forecasts during the active NAO– periods. The EABL forecasts initialised on ATLR show the lowest skill, followed by the NAO– (EABL) forecasts initialised on NAO+ or ATLR (NAO+). These results suggest that the recent models still have difficulties in predicting the onset of blocking.

Description: Sting jets (SJ) occur as an additional region of low-level strong winds in some Shapiro-Keyser-type extra-tropical cyclones. While SJs are widely accepted as being distinct from the warm and cold conveyor belts, the mechanisms responsible for their occurrence are still not fully understood. Here we determine the relative importance of the release of mesoscale instabilities and synoptic-scale cyclone dynamics, so addressing an area of current debate. Numerical weather prediction simulations of a SJ-containing windstorm are analysed and Lagrangian trajectories used to assess the evolution of, and mesoscale atmospheric instabilities (e.g. symmetric and inertial instabilities) in, the descending airstream. The SJ undergoes a two-stage descent: cooling via sublimation followed by a large acceleration accompanied by instability release. Combined tilting and stretching of vorticity play a major role in the local onset of instability on the airstream. Vorticity and frontogenesis fields have a narrow slantwise banded structure in the cloud head and around the SJ; the descending SJ modifies the widespread frontolysis expected from the large-scale dynamics alone in the frontal-fracture region. A coarser-resolution simulation also generates strong winds in the frontal-fracture region, although these are significantly weaker than in the higher-resolution simulation. The SJ airstream in the coarser-resolution simulation undergoes a weaker descent without instability generation and descends in a widespread frontolytic region. Hence, while the SJ undergoes a process of destabilisation that enhances its descent and acceleration in the higher-resolution simulation, enhancing the strong winds already generated by the synoptic-scale cyclone dynamics, this destabilisation does not occur in the SJ produced by a coarser-resolution simulation, resulting in weaker winds. This analysis reveals the synergy between the paradigms of SJ occurrence through the release of mesoscale instabilities and synoptic-scale cyclone dynamics and demonstrates that the current debate may in part be a consequence of the model resolutions used by different studies.

Description: The East African Long Rains season is unusual in that its year-to-year rainfall variability is mostly insensitive to the main modes of interannual tropical SST variability (ENSO, Indian Ocean dipole). Various alternative drivers of interannual variability have been described previously but remain poorly understood. Here we present an analysis of three important drivers: regional Indian Ocean SST, seasonal amplitude of the Madden-Julian oscillation (MJO) and phase of the quasi-biennial oscillation (QBO). Reanalyses and instrumental datasets are in close agreement about rainfall interannual variability across the region as a whole, which represents 30-50% of the total variance. Sub-regional structure of the remaining variance is far more uncertain and is not considered here. We use modern reanalyses to understand how the proposed drivers affect March-April mean. Common to all three drivers is their ability to modify the large-scale subsidence over the East African region during boreal spring. SST in the western Indian Ocean achieves this via anomalous boundary layer heating of the lower troposphere. The MJO modifies subsidence over the region through anomalous ascent and descent. Rainfall over East Africa responds to this MJO forcing in a uni-directional way, allowing seasonal rectification and interannual modulation by seasonal MJO amplitude. Understanding the QBO’s influence is complicated by the limited number of cycles over the reanalysis period. Each driver individually has a modest effect on the Long Rains, but added together they explain 30-60% of the variance of yearly rainfall variability that affects the region as a whole. This constitutes 13-25% of the total interannual precipitation variance, depending on dataset. The mechanisms we discuss suggest priorities for model development to improve model variability over East Africa. The metrics developed here lend themselves for easy evaluation of the remote drivers in models and other datasets.

Description: The SAPHIR instrument provides improved sampling of tropical atmospheric moisture vertically, horizontally and temporally. The impact of these unique characteristics is investigated through: an idealised study of retrieved humidity profiles; single observation experiments; assimilation experiments in a global NWP system; and an investigation into the spin-down of precipitation in the early phase of the forecast. SAPHIR offers improved performance over similar satellite instruments and beneficial impacts were found in all investigations. When assimilated in conjunction with observations from the microwave imager AMSR-2 the impact is further improved. Retrieval studies showed the errors in retrievals from SAPHIR were lower than those obtained from MHS or ATMS at all levels above 600 hPa. Single observation experiments showed that, when assimilated together with AMSR-2, AMSR-2 driven humidity increments were modified to give more realistic vertical structure. In assimilation experiments employing a near-operational configuration of the Met Office global model, the assimilation of clear-sky SAPHIR data improved the root-mean-square errors of a number of forecast metrics, most notably temperature at 250 hPa (improved by 2%), relative humidity at 500 hPa (2%) and wind at 500 hPa (1%) at forecast lead times of 12 and 24 hours. The results of this work form a clear recommendation for future remote sensing missions including both SAPHIR and AMSR-2 channel configurations.