Deep Convective System Evolution over Africa and the Tropical Atlantic

In the tropical African and neighboring Atlantic region there is a strong contrast in the properties of deep convection between land and ocean. Here, satellite radar observations are used to produce a composite picture of the life cycle of convection in these two regions. Estimates of the broadband thermal flux from the geostationary Meteosat-8 satellite are used to identify and track organized convective systems over their life cycle. The evolution of the system size and vertical extent are used to define five life cycle stages (warm and cold developing, mature, cold and warm dissipating), providing the basis for the composite analysis of the system evolution. The tracked systems are matched to overpasses of the Tropical Rainfall Measuring Mission satellite, and a composite picture of the evolution of various radar and lightning characteristics is built up. The results suggest a fundamental difference in the convective life cycle between land and ocean. African storms evolve from convectively active systems with frequent lightning in their developing stages to more stratiform conditions as they dissipate. Over the Atlantic, the convective fraction remains essentially constant into the dissipating stages, and lightning occurrence peaks late in the life cycle. This behavior is consistent with differences in convective sustainability in land and ocean regions as proposed in previous studies. The area expansion rate during the developing stages of convection is used to provide an estimate of the intensity of convection. Reasonable correlations are found between this index and the convective system lifetime, size, and depth.

Precipitation distributions for explicit versus parameterized convection in a large-domain high-resolution tropical case study

Global climate and weather models tend to produce rainfall that is too light and too regular over the tropical ocean. This is likely because of convective parametrizations, but the problem is not well understood. Here, distributions of precipitation rates are analyzed for high-resolution UK Met Office Unified Model simulations of a 10 day case study over a large tropical domain (∼20°S–20°N and 42°E–180°E). Simulations with 12 km grid length and parametrized convection have too many occurrences of light rain and too few of heavier rain when interpolated onto a 1° grid and compared with Tropical Rainfall Measuring Mission (TRMM) data. In fact, this version of the model appears to have a preferred scale of rainfall around 0.4 mm h−1 (10 mm day−1), unlike observations of tropical rainfall. On the other hand, 4 km grid length simulations with explicit convection produce distributions much more similar to TRMM observations. The apparent preferred scale at lighter rain rates seems to be a feature of the convective parametrization rather than the coarse resolution, as demonstrated by results from 12 km simulations with explicit convection and 40 km simulations with parametrized convection. In fact, coarser resolution models with explicit convection tend to have even more heavy rain than observed. Implications for models using convective parametrizations, including interactions of heating and moistening profiles with larger scales, are discussed. One important implication is that the explicit convection 4 km model has temperature and moisture tendencies that favour transitions in the convective regime. Also, the 12 km parametrized convection model produces a more stable temperature profile at its extreme high-precipitation range, which may reduce the chance of very heavy rainfall. Further study is needed to determine whether unrealistic precipitation distributions are due to some fundamental limitation of convective parametrizations or whether parametrizations can be improved, in order to better simulate these distributions.

Dynamic hydrological modeling in drylands with TRMM based rainfall

This paper introduces and evaluates DryMOD, a dynamic water balance model of the key hydrological process in drylands that is based on free, public-domain datasets. The rainfall model of DryMOD makes optimal use of spatially disaggregated Tropical Rainfall Measuring Mission (TRMM) datasets to simulate hourly rainfall intensities at a spatial resolution of 1-km. Regional-scale applications of the model in seasonal catchments in Tunisia and Senegal characterize runoff and soil moisture distribution and dynamics in response to varying rainfall data inputs and soil properties. The results highlight the need for hourly-based rainfall simulation and for correcting TRMM 3B42 rainfall intensities for the fractional cover of rainfall (FCR). Without FCR correction and disaggregation to 1 km, TRMM 3B42 based rainfall intensities are too low to generate surface runoff and to induce substantial changes to soil moisture storage. The outcomes from the sensitivity analysis show that topsoil porosity is the most important soil property for simulation of runoff and soil moisture. Thus, we demonstrate the benefit of hydrological investigations at a scale, for which reliable information on soil profile characteristics exists and which is sufficiently fine to account for the heterogeneities of these. Where such information is available, application of DryMOD can assist in the spatial and temporal planning of water harvesting according to runoff-generating areas and the runoff ratio, as well as in the optimization of agricultural activities based on realistic representation of soil moisture conditions.

Comparing tropical precipitation simulated by the Met Office NWP and climate models with satellite observations

Forecasts of precipitation and water vapor made by the Met Office global numerical weather prediction (NWP) model are evaluated using products from satellite observations by the Special Sensor Microwave Imager/Sounder (SSMIS) and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) for June–September 2011, with a focus on tropical areas (308S–308N). Consistent with previous studies, the predicted diurnal cycle of precipitation peaks too early (by ;3 h) and the amplitude is too strong over both tropical ocean and land regions. Most of the wet and dry precipitation biases, particularly those over land, can be explained by the diurnal-cycle discrepancies. An overall wet bias over the equatorial Pacific and Indian Oceans and a dry bias over the western Pacific warmpool and India are linked with similar biases in the climate model, which shares common parameterizations with the NWP version. Whereas precipitation biases develop within hours in the NWP model, underestimates in water vapor (which are assimilated by the NWP model) evolve over the first few days of the forecast. The NWP simulations are able to capture observed daily-to-intraseasonal variability in water vapor and precipitation, including fluctuations associated with tropical cyclones.

The effect of the Madden-Julian Oscillation on station rainfall and river level in the Fly River system, Papua New Guinea

The Madden-Julian oscillation (MJO) is the dominant mode of intraseasonal variability in tropical rainfall on the large scale, but its signal is often obscured in individual station data, where effects are most directly felt at the local level. The Fly River system, Papua New Guinea, is one of the wettest regions on Earth and is at the heart of the MJO envelope. A 16 year time series of daily precipitation at 15 stations along the river system exhibits strong MJO modulation in rainfall. At each station, the difference in rainfall rate between active and suppressed MJO conditions is typically 40% of the station mean. The spread of rainfall between individual MJO events was small enough such that the rainfall distributions between wet and dry phases of the MJO were clearly separated at the catchment level. This implies that successful prediction of the large-scale MJO envelope will have a practical use for forecasting local rainfall. In the steep topography of the New Guinea Highlands, the mean and MJO signal in station precipitation is twice that in the satellite Tropical Rainfall Measuring Mission 3B42HQ product, emphasizing the need for ground-truthing satellite-based precipitation measurements. A clear MJO signal is also present in the river level, which peaks simultaneously with MJO precipitation input in its upper reaches but lags the precipitation by approximately 18 days on the flood plains.

Investigation of discrepancies in satellite rainfall estimates over Ethiopia

Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mountainous region of Ethiopia. Overall, TAMSAT exhibits good skill in detecting rainy events but underestimates rainfall amount, while ARC underestimates both rainfall amount and rainy event frequency. Meanwhile, TRMM consistently performs best in detecting rainy events and capturing the mean rainfall and seasonal variability, while CMORPH tends to overdetect rainy events. Moreover, the mean difference in daily rainfall between the products and rain gauges shows increasing underestimation with increasing elevation. However, the distribution in satellite–gauge differences demon- strates that although 75% of retrievals underestimate rainfall, up to 25% overestimate rainfall over all eleva- tions. Case studies using high-resolution simulations suggest underestimation in the satellite algorithms is likely due to shallow convection with warm cloud-top temperatures in addition to beam-filling effects in microwave- based retrievals from localized convective cells. The overestimation by IR-based algorithms is attributed to nonraining cirrus with cold cloud-top temperatures. These results stress the importance of understanding re- gional precipitation systems causing uncertainties in satellite rainfall estimates with a view toward using this knowledge to improve rainfall algorithms.

Coupled simulations of the Indian monsoon intraseasonal oscillation using a fine-resolution mixed-layer ocean model

The intraseasonal variability (ISV) of the Indian summer monsoon is dominated by a 30–50 day oscillation between “active” and “break” events of enhanced and reduced rainfall over the subcontinent, respectively. These organized convective events form in the equatorial Indian Ocean and propagate north to India. Atmosphere–ocean coupled processes are thought to play a key role the intensity and propagation of these events. A high-resolution, coupled atmosphere–mixed-layer-oceanmodel is assembled: HadKPP. HadKPP comprises the Hadley Centre Atmospheric Model (HadAM3) and the K Profile Parameterization (KPP) mixed-layer ocean model. Following studies that upper-ocean vertical resolution and sub-diurnal coupling frequencies improve the simulation of ISV in SSTs, KPP is run at 1 m vertical resolution near the surface; the atmosphere and ocean are coupled every three hours. HadKPP accurately simulates the 30–50 day ISV in rainfall and SSTs over India and the Bay of Bengal, respectively, but suffers from low ISV on the equator. This is due to the HadAM3 convection scheme producing limited ISV in surface fluxes. HadKPP demonstrates little of the observed northward propagation of intraseasonal events, producing instead a standing oscillation. The lack of equatorial ISV in convection in HadAM3 constrains the ability of KPP to produce equatorial SST anomalies, which further weakens the ISV of convection. It is concluded that while atmosphere–ocean interactions are undoubtedly essential to an accurate simulation of ISV, they are not a panacea for model deficiencies. In regions where the atmospheric forcing is adequate, such as the Bay of Bengal, KPP produces SST anomalies that are comparable to the Tropical Rainfall Measuring Mission Microwave Imager (TMI) SST analyses in both their magnitude and their timing with respect to rainfall anomalies over India. HadKPP also displays a much-improved phase relationship between rainfall and SSTs over a HadAM3 ensemble forced by observed SSTs, when both are compared to observations. Coupling to mixed-layer models such as KPP has the potential to improve operational predictions of ISV, particularly when the persistence time of SST anomalies is shorter than the forecast lead time.

Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations

A new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) over the ocean is presented, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain-rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes’s theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance the understanding of theoretical benefits of the Bayesian approach, sensitivity analyses have been conducted based on two synthetic datasets for which the “true” conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism, but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak owing to saturation effects. It is also suggested that both the choice of the estimators and the prior information are crucial to the retrieval. In addition, the performance of the Bayesian algorithm herein is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate.

Exploring the land–ocean contrast in convective vigor using islands

Moist convection is well known to be generally more intense over continental than maritime regions, with larger updraft velocities, graupel, and lightning production. This study explores the transition from maritime to continental convection by comparing the trends in Tropical Rainfall Measuring Mission (TRMM) radar and microwave (37 and 85 GHz) observations over islands of increasing size to those simulated by a cloud-resolving model. The observed storms were essentially maritime over islands of <100 km2 and continental over islands >10 000 km2, with a gradual transition in between. Equivalent radar and microwave quantities were simulated from cloud-resolving runs of the Weather Research and Forecasting model via offline radiation codes. The model configuration was idealized, with islands represented by regions of uniform surface heat flux without orography, using a range of initial sounding conditions without strong horizontal winds or aerosols. Simulated storm strength varied with initial sounding, as expected, but also increased sharply with island size in a manner similar to observations. Stronger simulated storms were associated with higher concentrations of large hydrometeors. Although biases varied with different ice microphysical schemes, the trend was similar for all three schemes tested and was also seen in 2D and 3D model configurations. The successful reproduction of the trend with such idealized forcing supports previous suggestions that mesoscale variation in surface heating—rather than any difference in humidity, aerosol, or other aspects of the atmospheric state—is the main reason that convection is more intense over continents and large islands than over oceans. Some dynamical storm aspects, notably the peak rainfall and minimum surface pressure low, were more sensitive to surface forcing than to the atmospheric sounding or ice scheme. Large hydrometeor concentrations and simulated microwave and radar signatures, however, were at least as sensitive to initial humidity levels as to surface forcing and were more sensitive to the ice scheme. Issues with running the TRMM simulator on 2D simulations are discussed, but they appear to be less serious than sensitivities to model microphysics, which were similar in 2D and 3D. This supports the further use of 2D simulations to economically explore modeling uncertainties.

Quantitative precipitation estimation over ocean using Bayesian approach from microwave observations during the typhoon season

We have developed a new Bayesian approach to retrieve oceanic rain rate from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), with an emphasis on typhoon cases in the West Pacific. Retrieved rain rates are validated with measurements of rain gauges located on Japanese islands. To demonstrate improvement, retrievals are also compared with those from the TRMM/Precipitation Radar (PR), the Goddard Profiling Algorithm (GPROF), and a multi-channel linear regression statistical method (MLRS). We have found that qualitatively, all methods retrieved similar horizontal distributions in terms of locations of eyes and rain bands of typhoons. Quantitatively, our new Bayesian retrievals have the best linearity and the smallest root mean square (RMS) error against rain gauge data for 16 typhoon overpasses in 2004. The correlation coefficient and RMS of our retrievals are 0.95 and ~2 mm hr-1, respectively. In particular, at heavy rain rates, our Bayesian retrievals outperform those retrieved from GPROF and MLRS. Overall, the new Bayesian approach accurately retrieves surface rain rate for typhoon cases. Accurate rain rate estimates from this method can be assimilated in models to improve forecast and prevent potential damages in Taiwan during typhoon seasons.

A comparative performance analysis of TRMM 3B42 (TMPA) versions 6 and 7 for hydrological applications over Andean-Amazon river basins

The Tropical Rainfall Measuring Mission 3B42 precipitation estimates are widely used in tropical regions for hydrometeorological research. Recently, version 7 of the product was released. Major revisions to the algorithm involve the radar refl ectivity - rainfall rates relationship, surface clutter detection over high terrain, a new reference database for the passive microwave algorithm, and a higher quality gauge analysis product for monthly bias correction. To assess the impacts of the improved algorithm, we compare the version 7 and the older version 6 product with data from 263 rain gauges in and around the northern Peruvian Andes. The region covers humid tropical rainforest, tropical mountains, and arid to humid coastal plains. We and that the version 7 product has a significantly lower bias and an improved representation of the rainfall distribution. We further evaluated the performance of versions 6 and 7 products as forcing data for hydrological modelling, by comparing the simulated and observed daily streamfl ow in 9 nested Amazon river basins. We find that the improvement in the precipitation estimation algorithm translates to an increase in the model Nash-Sutcliffe effciency, and a reduction in the percent bias between the observed and simulated flows by 30 to 95%.

Weakened tropical circulation and reduced precipitation in response to geoengineering

Geoengineering by injection of reflective aerosols into the stratosphere has been proposed as a way to counteract the warming effect of greenhouse gases by reducing the intensity of solar radiation reaching the surface. Here, climate model simulations are used to examine the effect of geoengineering on the tropical overturning circulation. The strength of the circulation is related to the atmospheric static stability and has implications for tropical rainfall. The tropical circulation is projected to weaken under anthropogenic global warming. Geoengineering with stratospheric sulfate aerosol does not mitigate this weakening of the circulation. This response is due to a fast adjustment of the troposphere to radiative heating from the aerosol layer. This effect is not captured when geoengineering is modelled as a reduction in total solar irradiance, suggesting caution is required when interpreting model results from solar dimming experiments as analogues for stratospheric aerosol geoengineering.

Sea-breeze dynamics and convection initiation: the influence of convective parameterization in weather and climate model biases

There are some long-established biases in atmospheric models that originate from the representation of tropical convection. Previously, it has been difficult to separate cause and effect because errors are often the result of a number of interacting biases. Recently, researchers have gained the ability to run multiyear global climate model simulations with grid spacings small enough to switch the convective parameterization off, which permits the convection to develop explicitly. There are clear improvements to the initiation of convective storms and the diurnal cycle of rainfall in the convection-permitting simulations, which enables a new process-study approach to model bias identification. In this study, multiyear global atmosphere-only climate simulations with and without convective parameterization are undertaken with the Met Office Unified Model and are analyzed over the Maritime Continent region, where convergence from sea-breeze circulations is key for convection initiation. The analysis shows that, although the simulation with parameterized convection is able to reproduce the key rain-forming sea-breeze circulation, the parameterization is not able to respond realistically to the circulation. A feedback of errors also occurs: the convective parameterization causes rain to fall in the early morning, which cools and wets the boundary layer, reducing the land–sea temperature contrast and weakening the sea breeze. This is, however, an effect of the convective bias, rather than a cause of it. Improvements to how and when convection schemes trigger convection will improve both the timing and location of tropical rainfall and representation of sea-breeze circulations.

Tropical-extratropical interactions over Southern Africa: three cases of heavy summer season rainfall

The synoptic evolution of three tropical–extratropical (TE) interactions, each responsible for extreme rainfall events over southern Africa, is discussed in detail. Along with the consideration of previously studied events, common features of these heavy rainfall producing tropical temperate troughs (TTTs) over southern Africa are discussed. It is found that 2 days prior to an event, northeasterly moisture transports across Botswana, set up by the Angola low, are diverted farther south into the semiarid region of subtropical southern Africa. The TTTs reach full maturity as a TE cloud band, rooted in the central subcontinent, which is triggered by upper-level divergence along the leading edge of an upper-tropospheric westerly wave trough. Convection and rainfall within the cloud band is supported by poleward moisture transports with subtropical air rising as it leaves the continent and joins the midlatitude westerly flow. It is shown that these systems fit within a theoretical framework describing similar TE interactions found globally. Uplift forcing for the extreme rainfall of each event is investigated. Unsurprisingly, quasigeostrophic uplift is found to dominate in the midlatitudes with convective processes strongest in the subtropics. Rainfall in the semiarid interior of South Africa appears to be a result of quasigeostrophically triggered convection. Investigation of TTT formation in the context of planetary waves shows that early development is sometimes associated with previous anticyclonic wave breaking south of the subcontinent, with full maturity of TTTs occurring as a potential vorticity trough approaches the continent from the west. Sensitivity to upstream wave perturbations and effects on anticyclonic wave breaking in the South Indian Ocean are also observed.