Chien Wang

Global change poses “wicked problems” that have become ever more complex, pervasive, and damaging. Developing innovative solutions increasingly require diverse research approaches. The Franco-German Make Our Planet Great Again (MOPGA) program was designed to create a unique international network of top-level research, from fundamental to solution-oriented projects. MOPGA stands out from other large research initiatives by focusing not on a singular central research challenge but on facilitating multidisciplinary interactions between traditionally separated fields. MOPGA recognized that social, natural and engineering sciences share a unifying aim to address global change. In addition to addressing timely and innovative research questions within disciplines, MOPGA worked to improve communication across disciplines via annual meetings for all laureates and their research groups, scientific board exchanges, and public online seminars. Drawing on our MOPGA experiences, we discuss how such exchanges should be extended to meet the needs identified by the scientific community, international policy-makers, and regional stakeholders. In the current political landscape of scientific suppression and heightened mistrust in scientific expertise, the need for such bold, independent and collaborative scientific initiatives is greater than ever.

A full two-moment microphysics parameterization of the LIMA scheme (with LIMA standing for Liquid Ice Multiple Aerosols and hereafter named LIMA v2.0) has been developed and successfully implemented in the Meso-NH cloud-resolving model. The novelty of the scheme is a set of prognostic equations of the number concentration of each precipitating ice category (snow/aggregates, graupel, and hail), in addition to the prediction of the mass mixing ratios. As a result, new microphysical conversion rates are introduced and explicitly computed using the size distributions of the hydrometeors. The new LIMA v2.0 scheme has been tested for an idealized deep convection case against the original LIMA scheme characterized by an empirical number-concentration–mixing-ratio relationship applied to the precipitating ice. Inclusion of number concentration equations for the snow/aggregates and graupel significantly alters the microphysical structure and dynamical evolution of the simulated supercell. When comparing to the results obtained with the previous version of LIMA, the new v2.0 of the scheme tends to increase the pristine ice mixing ratio; to decrease the other ice hydrometeors by slowing down the growing processes of snow/aggregates, graupel, and hail; and to enhance the feedbacks between raindrops and the ice-phase hydrometeors. This comparison also emphasizes the unreasonable diagnostic approach used to estimate the number concentration of precipitating ice particles in the previous version of the scheme. The new scheme is more efficient at producing earlier raindrops at ground level and reducing hail precipitation.

A convolutional neural network (CNN) has been developed to forecast the occurrence of low-visibility events or hazes in the Paris area. It has been trained and validated using multi-decadal daily regional maps of many meteorological and hydrological variables alongside surface visibility observations. The strategy is to make the machine learn from available historical data to recognize various regional weather and hydrological regimes associated with low-visibility events. To better preserve the characteristic spatial information of input features in training, two branched architectures have recently been developed. These architectures process input features firstly through several branched CNNs with different kernel sizes to better preserve patterns with certain characteristic spatial scales. The outputs from the first part of the network are then processed by the second part, a deep non-branched CNN, to further deliver predictions. The CNNs with new architectures have been trained using data from 1975 to 2019 in a two-class (haze versus non-haze) classification mode as well as a regression mode that directly predicts the value of surface visibility. The predictions of regression have also been used to perform the two-class classification forecast using the same definition in the classification mode. This latter procedure is found to deliver a much better performance in making class-based forecasts than the direct classification machine does, primarily by reducing false alarm predictions. The branched architectures have improved the performance of the networks in the validation and also in an evaluation using the data from 2021 to 2023 that have not been used in the training and validation. Specifically, in the latter evaluation, branched machines captured 70% of the observed low-visibility events during the three-year period at Charles de Gaulle Airport. Among those predicted low-visibility events by the machines, 74% of them are true cases based on observation.

A full two-moment microphysics parameterisation of the LIMA scheme (hereafter named LIMA v2.0) has been developed and successfully implemented in the Meso-NH cloud-resolving model. The novelty of the scheme is a set of prognostic equations of the number concentration of each precipitating ice category (snow-aggregates, graupel and hail), in complement to the prediction of the mass mixing ratios. As a result, new microphysical conversion rates are introduced and explicitly computed using the size distributions of the hydrometeors. The new LIMA v2.0 scheme has been tested for an idealized deep convection case against the original LIMA scheme characterized by an empirical number concentration-mixing ratio relationship applied to the precipitating ice. Inclusion of number concentration equations for the snow-aggregates and graupel significantly alters the microphysical structure and dynamical evolution of the simulated supercell. When comparing to the results obtained with the previous version of LIMA, the new v2.0 of the scheme tends to increase the pristine ice mixing ratio, to decrease the other ice hydrometeors, and to enhance the feedbacks between raindrops and the ice phase. The new scheme is also more efficient to produce earlier raindrops at ground level and to reduce hail precipitation.

A full two-moment microphysics parameterisation of the LIMA scheme (hereafter named LIMA v2.0) has been developed and successfully implemented in the Meso-NH cloud-resolving model. The novelty of the scheme is a set of prognostic equations of the number concentration of each precipitating ice category (snow-aggregates, graupel and hail), in complement to the prediction of the mass mixing ratios. As a result, new microphysical conversion rates are introduced and explicitly computed using the size distributions of the hydrometeors. The new LIMA v2.0 scheme has been tested for an idealized deep convection case against the original LIMA scheme characterized by an empirical number concentration-mixing ratio relationship applied to the precipitating ice. Inclusion of number concentration equations for the snow-aggregates and graupel significantly alters the microphysical structure and dynamical evolution of the simulated supercell. When comparing to the results obtained with the previous version of LIMA, the new v2.0 of the scheme tends to increase the pristine ice mixing ratio, to decrease the other ice hydrometeors, and to enhance the feedbacks between raindrops and the ice phase. The new scheme is also more efficient to produce earlier raindrops at ground level and to reduce hail precipitation.

A full two-moment microphysics parameterisation of the LIMA scheme (hereafter named LIMA v2.0) has been developed and successfully implemented in the Meso-NH cloud-resolving model. The novelty of the scheme is a set of prognostic equations of the number concentration of each precipitating ice category (snow-aggregates, graupel and hail), in complement to the prediction of the mass mixing ratios. As a result, new microphysical conversion rates are introduced and explicitly computed using the size distributions of the hydrometeors. The new LIMA v2.0 scheme has been tested for an idealized deep convection case against the original LIMA scheme characterized by an empirical number concentration-mixing ratio relationship applied to the precipitating ice. Inclusion of number concentration equations for the snow-aggregates and graupel significantly alters the microphysical structure and dynamical evolution of the simulated supercell. When comparing to the results obtained with the previous version of LIMA, the new v2.0 of the scheme tends to increase the pristine ice mixing ratio, to decrease the other ice hydrometeors, and to enhance the feedbacks between raindrops and the ice phase. The new scheme is also more efficient to produce earlier raindrops at ground level and to reduce hail precipitation.

A full two-moment microphysics parameterisation of the LIMA scheme (hereafter named LIMA v2.0) has been developed and successfully implemented in the Meso-NH cloud-resolving model. The novelty of the scheme is a set of prognostic equations of the number concentration of each precipitating ice category (snow-aggregates, graupel and hail), in complement to the prediction of the mass mixing ratios. As a result, new microphysical conversion rates are introduced and explicitly computed using the size distributions of the hydrometeors. The new LIMA v2.0 scheme has been tested for an idealized deep convection case against the original LIMA scheme characterized by an empirical number concentration-mixing ratio relationship applied to the precipitating ice. Inclusion of number concentration equations for the snow-aggregates and graupel significantly alters the microphysical structure and dynamical evolution of the simulated supercell. When comparing to the results obtained with the previous version of LIMA, the new v2.0 of the scheme tends to increase the pristine ice mixing ratio, to decrease the other ice hydrometeors, and to enhance the feedbacks between raindrops and the ice phase. The new scheme is also more efficient to produce earlier raindrops at ground level and to reduce hail precipitation.

A deep convolutional neural network has been developed to forecast the occurrence of the low visibility events or hazes in Paris. It is trained by using multi-decadal daily regional maps of various meteorological and hydrological variables as the input features and surface visibility observations as the targets. To better preserve the characteristic spatial-scale information of different input features during the training, two branched architectures have recently been developed. These new architectures have improved the performance of the network, producing impressive scores in validation and also in an evaluation using the data of 2021 and 2022 that have not been used in the training and validation.

Northern India has undergone intense urbanization since the middle of the 20th century. The impact of such drastic land-use change on the regional weather and climate remains to be assessed. In this work, we study the impact of the modification of land use – from vegetation to urban – on the Indian summer monsoon rainfall as well as on other meteorological variables. We use the regional Meso-scale Non-Hydrostatic (Meso-NH) model coupled with an urban module (the Town Energy Balance model) to perform monthlong sensitivity simulations centered around Kolkata, the most urbanized area in northeastern India. Paired simulations, one with and another without urban settings, have been performed to identify the impacts related to urbanization through both thermodynamic and kinetic effects. We find that the perturbation induced by urban land use enhances the mean rainfall over the model domain, principally by intensifying the convective activity through thermodynamic perturbation, leading to a 14.4 % increase in the monthly mean rainfall. The urban area also induces a 15.0 % rainfall increase during two modeled periods of heavy precipitation caused by low-pressure systems. In addition, the modeling results demonstrate that the urban area not only generally acts as a rainfall enhancer, particularly during nighttime, but also induces the generation of a specific storm in one modeled case that would not have formed in the absence of the urban area. The initiation of this storm over the city was primarily due to the urban terrain's disturbance of the near-surface wind flow, leading to a surge in dynamically produced turbulent kinetic energy (TKE). The thermal production of TKE over the nighttime urban boundary layer, on the other hand, serves as a contributing factor to the storm formation.

Northern India has been subjected to an intense urbanization since the middle of the twentieth century. The impact of such a drastic land-use change on regional weather and climate remains to be assessed. In this work, we study the impact of the modification of land-use – from vegetation to urban – on the Indian summer monsoon rainfall, as well as other meteorological variables. We use the regional meteorology model Meso-NH coupled with an urban module (the Town Energy Balance model) to perform month-long sensitivity simulations centered around Kolkata, the most urbanized area in Northeastern India. Paired simulations, one with and another without urban settings have been performed to identify the impacts related to urbanization through both thermodynamic and kinetic effects. We find that the perturbation induced by urban land-use enhances the mean rainfall over the model domain, principally by intensifying the convective activity through thermodynamical perturbation, leading to an increase of 14.4 % of the monthly mean rainfall. In addition, the modeling results demonstrate that not only does the urban area act in general as a rainfall enhancer, particularly during nighttime, but it also induces the generation of a specific storm in one modeled case that would not have formed in the absence of the urban area. This storm initiation over the city was done primarily by urban terrain’s disturbance of the near surface wind flow, leading to a surge of dynamically produced turbulent kinetic energy (TKE). The thermal production of TKE over the nighttime urban boundary layer, on the other hand, serves as a contributing factor to the storm formation.